EP2013761A2 - Prédiction contextuelle de mots d'utilisateur et d'actions d'utilisateur - Google Patents

Prédiction contextuelle de mots d'utilisateur et d'actions d'utilisateur

Info

Publication number
EP2013761A2
EP2013761A2 EP07760919A EP07760919A EP2013761A2 EP 2013761 A2 EP2013761 A2 EP 2013761A2 EP 07760919 A EP07760919 A EP 07760919A EP 07760919 A EP07760919 A EP 07760919A EP 2013761 A2 EP2013761 A2 EP 2013761A2
Authority
EP
European Patent Office
Prior art keywords
user
actions
word
cues
cdb
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
EP07760919A
Other languages
German (de)
English (en)
Other versions
EP2013761B1 (fr
EP2013761A4 (fr
Inventor
Ethan R. Bradford
Michael Longe
Pim Van Meurs
David Jon Kay
Gaurav Tandon
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tegic Communications Inc
Original Assignee
Tegic Communications Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tegic Communications Inc filed Critical Tegic Communications Inc
Publication of EP2013761A2 publication Critical patent/EP2013761A2/fr
Publication of EP2013761A4 publication Critical patent/EP2013761A4/fr
Application granted granted Critical
Publication of EP2013761B1 publication Critical patent/EP2013761B1/fr
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
    • G06F3/0481—Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
    • G06F3/0481—Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
    • G06F3/0482—Interaction with lists of selectable items, e.g. menus
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/274—Converting codes to words; Guess-ahead of partial word inputs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for program control, e.g. control units
    • G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
    • G06F9/44—Arrangements for executing specific programs
    • G06F9/455—Emulation; Interpretation; Software simulation, e.g. virtualisation or emulation of application or operating system execution engines
    • G06F9/45504—Abstract machines for programme code execution, e.g. Java virtual machine [JVM], interpreters, emulators
    • G06F9/45508—Runtime interpretation or emulation, e g. emulator loops, bytecode interpretation
    • G06F9/45512—Command shells
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00—Computing arrangements using knowledge-based models
    • G06N5/04—Inference or reasoning models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M1/00—Substation equipment, e.g. for use by subscribers
    • H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
    • H04M1/724—User interfaces specially adapted for cordless or mobile telephones
    • H04M1/72448—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions
    • H04M1/72454—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions according to context-related or environment-related conditions
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M1/00—Substation equipment, e.g. for use by subscribers
    • H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
    • H04M1/724—User interfaces specially adapted for cordless or mobile telephones
    • H04M1/72448—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions
    • H04M1/72451—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions according to schedules, e.g. using calendar applications
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M1/00—Substation equipment, e.g. for use by subscribers
    • H04M1/72—Mobile telephones; Cordless telephones, i.e. devices for establishing wireless links to base stations without route selection
    • H04M1/724—User interfaces specially adapted for cordless or mobile telephones
    • H04M1/72448—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions
    • H04M1/72457—User interfaces specially adapted for cordless or mobile telephones with means for adapting the functionality of the device according to specific conditions according to geographic location
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04M—TELEPHONIC COMMUNICATION
    • H04M2250/00—Details of telephonic subscriber devices
    • H04M2250/10—Details of telephonic subscriber devices including a GPS signal receiver

Definitions

  • This invention relates to handheld computing devices. More particularly, the invention concerns a handheld computing device operable to automatically detect cues describing the device's environment and user actions performed with the device, learn which cues and cue combinations are relevant to predict user actions, and then in response to occurrence of the relevant cues, predictively implementing the appropriate user action or configuring the device in anticipation of user action.
  • some small devices employ a digitizing surface to receive users' handwriting. This approach permits users to write naturally, albeit in a small area as permitted by the size of the portable computer. Based upon the user's contact with the digitizing surface, handwriting recognition algorithms analyze the geometric characteristics of the user's entry to determine each character or word.
  • touch-sensitive panels on which some type of keyboard overlay has been printed, or a touch-sensitive screen with a keyboard overlay displayed. The user employs a finger or a stylus to interact with the panel or display screen in the area associated with the desired key or letter.
  • an operating sequence manages a handheld computing to automatically detect cues describing the device's environment and user actions performed with the device, learn which cues and cue combinations are relevant to predict user actions, and then in response to occurrence of the relevant cues, predictively implementing the appropriate user action or configuring the device in anticipation of user action.
  • FIGURE 1 is a block diagram showing a plan exterior view of a computing device.
  • FIGURE 2 is a block diagram showing hardware, software, and interconnections of a computing device.
  • FIGURE 2A is a block diagram of a digital data processing machine.
  • FIGURE 2B shows an exemplary signal-bearing medium.
  • FIGURE 2C is a perspective view of exemplary logic circuitry.
  • One aspect of this disclosure concerns user entry of information into a system with an input device.
  • a scheme is provided in which an entire word that a user wants to enter is predicted after the user enters a specific symbol, such as a space character. If the user presses an ambiguous key thereafter, rather than accept the prediction, the selection list is reordered. For example, a user enters the phrase "Lets run to school. Better yet, lets drive to ".””” After the user presses the space, after first entering the second occurrence of the word "to,” the system predicts that the user is going to enter the word "school” based on the context in which the user has entered that word in the past.
  • predictions may be available if the user had previously entered text with the same context (for example, "to work”, “to camp”). These predictions are presented if the user presses the "next" key; the key specified for scrolling through the list. Should the user enter an ambiguous key after the space, then a word list is reordered to give precedence to the words that match context. For example, if the user presses the ambiguous key that contains the letters 'a', 'b', and 'c', the word "camp” is given precedence in the list. [1019]
  • the disclosed system can also make predictions on other forms of context, such as the person to whom the message is sent, the person writing the message, the day of the week, the time of the week, etc.
  • the system is passed a series of parameters by the device which may or may not be relevant and the system learns which of the parameters are relevant for prediction and which ones are not.
  • prediction may go beyond words and predict phrases. Prediction also may depend on grammar, semantics etc.
  • Other embodiments contemplate anticipation of user actions, as well as words and phrases, such as a user action in connection with menu items, or a user action in connection with form filling.
  • the knowledge gained from user patterns can be uploaded/downloaded and/or served from a server allowing this information to be shared between devices and applications.
  • NWP Next Word Prediction'
  • FIGURE 1 is a schematic representation of a device 14 having a display 10 and user information input mechanism 12, and that incorporates next word prediction technology as disclosed herein.
  • the user has entered the phrase "Lets run to school. Better yet, lets drive to.”
  • the user presses space after entering the word "to,” and the system predicts that the user is next going to enter the word "school,” based on the context in which the user has entered the word "school” in the past. In this case, only the previous word for the context is looked at.
  • the last time the user entered the word "to” he entered the word "school” directly after.
  • the user has entered the word "to” again, and the prediction word "school” is present.
  • context information comes from previous text entered in this message only.
  • context information is compiled from text entered in prior messages/sessions as well.
  • Predictions are made when the context in the current message matches the context in text the user previously entered.
  • the concept of context can be very general. Context can mean the nature of the text entered. Context can also be combined with other contexts, such as, for example: a) The person to whom a message is sent; b) The person writing the message; c) The day of the week; d) The time of day.
  • a further embodiment starts with a very broad set of possible factors and performs on-the-fly factor analysis of the user behavior to determine the most effective factor to include as context.
  • This system does more than adapt to user behavior based on a priori specified factors, such as text, recipient, author, day, that are recorded, but is also intelligent enough to determine which factors are most important and emphasize those. This allows for better prediction.
  • Another example of prediction is based upon time of day. For example, when entering a message "let's meet for" at lunchtime, the word “lunch” is automatically predicted as the next word in the phrase. Later in the day the word “dinner” is predicted.
  • the phrases stored also can have time of day associated with them as one of their attributes. This can be used to decide which phrases are relevant when the user is entering text.
  • Prediction can also be applied to other concepts as well, such as menus and user actions.
  • the context module When a user clicks a menu, the context module is provided with a keyword for that menu as the preceding context word. The context module then produces the entries previously selected from that menu because they are in the context database as entries preceded by that keyword, and those words can be reordered to the top of the menu.
  • the context module When a menu entry is selected, the context module then automatically notes it as having occurred with the menu tag as context for reordering to the front next time.
  • the last option would be "go to standard menu tree.” This way, the user is presented with the most likely next end state, rather than the most likely behavior directly from here, which in a normal phone would be going back to the menu tree. The user does not have to navigate a menu tree at all, but rather has one click (or no click) to go to the next task.
  • Form filling is another useful function, which may be performed by the disclosed system.
  • Context sensitivity by field attribute e.g. date only predicts months, day switches to numeric mode etc. This can similarly be applied to form input.
  • the browser, or other form-input software can provide the prompt for the input cell as context for text entry of the cell.
  • a form prompts for "Name:" the user's name is available with few to no keystrokes, and other names he might fill in on forms are also made easier to enter.
  • next word prediction may be applied to Tegic Corporation's T9® technology.
  • T9 technology combines the groups of letters found on each key of an input device, e.g. each phone key, with a fast-access dictionary of words, and recognizes what a user wants to input as text as he types.
  • T9 technology offers the most commonly-used word for every key sequence entered by default and then lets the user access other choices with one or more presses of the NEXT or space key.
  • T9 technology and “T9” systems.
  • T9 is a trademark, and certain products and earlier patents contain features marked under this mark, the present disclosure's citations to “T9” refer to represent a novel implementation of technology, and namely, the introduction of certain new features in the context of existing T9 technology. Accordingly, "T9” is not used as a reference to admitted prior art.
  • FIGURE 2 shows a block diagram of the various subcomponents and interconnections of a handheld computing device 101.
  • the device 101 may be implemented as a reduced keyboard disambiguating system.
  • a user input 54 and the display 53 are coupled to a processor 100 through appropriate interfacing circuitry.
  • a speaker 102 is also coupled to the processor.
  • Another optional component includes one or more sensors 55, similarly coupled to the CPU 100.
  • the user input 54 comprises a keyboard, telephone or other style keypad, soft keyboard, screen overlay keyboard, mouse, trackball, handwriting digitizing surface, or any other means for the user to communicate input into the machine 101. Without any intended limitation, the ensuing discussion uses an example where the input device 54 is a keyboard.
  • the processor 100 receives inputs from the keyboard, and manages all output to the display and speaker.
  • the processor 100 is coupled to digital data storage 104.
  • the storage 104 includes a combination of temporary storage media, such as random access memory (RAM), and permanent storage media, such as read-only memory (ROM), floppy disks, hard disks, or CD- ROMs.
  • the storage 104 contains all software routines necessary to govern system operation.
  • the memory contains an operating system 106, disambiguating software 108, associated filtering of ambiguous text entry software and/or extending and interpreting software 110, and a contextual database 116, the latter of which is discussed in additional detail below.
  • the memory also includes a vocabulary database 30.
  • the memory may contain one or more application programs 112, 114. Examples of application programs include word processors, software dictionaries, and foreign language translators. Speech synthesis software may also be provided as an application program, thereby allowing the reduced keyboard disambiguating system to function as a communication aid. Therefore, the output, in this case, might be vocally output from the speaker.
  • the storage 104 includes cues 123, actions 125, and a mapping 124 between the two.
  • the cues 123 describe environment of the device 101 and user performed actions of configuring the device and operating its application programs.
  • the actions 125 represent associated user actions involving the device 101.
  • the nature, meaning, and characteristics of the cues 123 and actions 125 are described in greater detail below in conjunction with the details operation of the system of FIGURE 2.
  • the cues 123 and actions 125 may be embodied in one or more linked lists, tables, relational databases, alphanumeric data streams, disk sector, file, physical or logical storage device, or any other useful storage construct.
  • the cues 123 and actions 125 are related to the process of automatically detecting cues describing the device's environment and user actions performed with the device, learning which cues and cue combinations are relevant to predict user actions, and then in response to occurrence of the relevant cues, predictively implementing the appropriate user action or configuring the device in anticipation of user action.
  • One aspect of the discussion herein concerns to symbols and sequences of symbols, which, when combined, make an object or part of an object.
  • a typical example of a symbol is a character in any language, such as a letter, digit, punctuation mark, or any other symbol from a language.
  • a typical example of an object or part of an object is a word or part of a word.
  • T9 systems comprise at least three components:
  • This component contains the user interface (Ul) and handles communications between the device and the T9 core. Communications can occur either through an event-based or a function- based API, discussed below.
  • a core engine for example the core engine known as the T9 core, which is provided by Tegic.
  • Alphabetic T9 and Chinese T9 implementations can include the following supplemental databases:
  • An Alphabetic T9 UDB contains custom words entered by the user. Typically, these are words that cannot be generated by the LDB, such as names, e-mail addresses, and instant messaging IDs. The database also contains information on how frequently a user selects words — both custom words and words from the LDB.
  • Context Database (Alphabetic T9).
  • An Alphabetic T9 CDB contains information on the words the user has previously entered. T9 requires this information for its next-word prediction and CDB word completion features.
  • the context database contains recently entered words. Alphabetic T9 uses this information to provide predicted and completed words in the selection list, and to reorder full and completed words in the selection list.
  • Alphabetic T9 When the user accepts the active word by entering a space (pressing keys that correspond to the T9 key value T9KEYSPACE) Alphabetic T9 performs the actions above, as well as the following actions:
  • data processing entities of this disclosure may be implemented in various forms.
  • One example is a digital data processing apparatus, as exemplified by the hardware components and interconnections of the digital data processing apparatus 200 of FIGURE 2A.
  • the apparatus 200 also includes an input/output 210, such as a line, bus, cable, electromagnetic link, or other means for the processor 202 to exchange data with other hardware external to the apparatus 200.
  • an input/output 210 such as a line, bus, cable, electromagnetic link, or other means for the processor 202 to exchange data with other hardware external to the apparatus 200.
  • the signal-bearing media may take various forms.
  • such a signal-bearing media may comprise, for example, the storage 204 or another signal-bearing media, such as an optical storage 250 of FIGURE 2B, directly or indirectly accessible by a processor 202.
  • the instructions may be stored on a variety of machine- readable data storage media.
  • Some examples include direct access storage (e.g., a conventional "hard drive”, redundant array of inexpensive disks (“RAID”), or another direct access storage device (“DASD”)), serial-access storage such as magnetic or optical tape, electronic non-volatile memory (e.g., ROM, EPROM, flash PROM, or EEPROM), battery backup RAM, optical storage (e.g., CD-ROM, WORM, DVD, digital optical tape), or other suitable machine readable signal-bearing media.
  • direct access storage e.g., a conventional "hard drive”, redundant array of inexpensive disks (“RAID”), or another direct access storage device (“DASD”)
  • serial-access storage such as magnetic or optical tape
  • electronic non-volatile memory e.g., ROM, EPROM, flash PROM, or EEPROM
  • battery backup RAM e.g., CD-ROM, WORM, DVD, digital optical tape
  • optical storage e.g., CD-ROM, WORM, DVD, digital optical tape
  • the CDB is searched for the next most recent occurrence of the word just entered (318). If found, the word following it in the database is presented as a prediction (306 and 308). If the user does not accept the word (310), and does not press the next key, no processing is complete, and T9 waits for next key entry (314), as further described in connection with FIGURE 4.
  • Alphabetic T9 creates a selection list of predicted words. The maximum number of predicted words in the selection list depends on the literal value of the #define constant T9MAXCDBMATCHES. Unless a different value is assigned, this constant is set to 6. [1061] The user selects and accepts a predicted word using the same process used in T9 for selecting and accepting a word. After the user accepts a predicted word (310), Alphabetic T9 processes the word (312). It will be appreciated by those skilled in the art that the disclosed system may be applied to other disambiguation systems than T9, as well as other forms of T9 than Alphabetic T9.
  • FIGURE 4 is a flow diagram showing the processing of words in an exemplary next word prediction method.
  • a CDB contains information on recently entered words.
  • Alphabetic T9 uses this information to include predicted and completed words in the selection list.
  • Alphabetic T9 checks its other databases only for words that match the current active key sequence
  • Alphabetic T9 also checks the CDB for the most recently accepted word, i.e. the most recently entered non-active word.
  • CDB words do not necessarily have to match the active word to be included in the selection list. For predicted words, which appear only when there is no active key sequence (in one embodiment), the CDB match depends on the word before the active word. For completed CDB words, the match depends on both the word before the active word and the key sequence of the active word.
  • Alphabetic T9 finds in the CDB the word the user has entered, Alphabetic T9 suggests the word that immediately follows in the CDB as a predicted word. For example, if the CDB contains the word pair "text message” and the user enters the word “text” and then presses the Space key, Alphabetic T9 places "message” in the selection list as a predicted word.
  • Alphabetic T9 finds in the CDB the word the user has entered, Alphabetic T9 suggests the word that immediately follows in the CDB as a completed word if the word matches the active key sequence, although the completed word contains additional characters. For example, if the CDB contains the word pair "text message" and the user enters the word “text,” adds a space, and then enters the key sequence 6-3-7-7, which corresponds to the first four letters in the word "message”, Alphabetic T9 places "message” in the selection list as a completed word. [1066] In one embodiment, CDB word completion operates independently of UDB custom-word completion, LDB word completion, and MDB word completion.
  • the integration layer should:
  • the integration layer must allocate persistent memory to store the CDB.
  • the integration layer activates CDB operations by calling T ⁇ AWCdbActivate, it copies the CDB from persistent memory to RAM.
  • the database is referenced as an instance of the CDB Data Structure (T9AWCdblnfo).
  • the integration layer must initialize all T9AWCdblnfo structure fields values to 0. If the integration layer has copied an existing CDB from persistent memory to
  • the integration layer activates CDB operations by calling T9AWCdbActivate.
  • the integration layer calls this function, it provides a pointer to an instance of the
  • CDB Data Structure (T9AWCdblnfo) for which it has allocated memory.
  • Alphabetic T9 searches the CDB for depends on whether there is an active key sequence:
  • Alphabetic T9 searches the CDB for words that match the key sequence.
  • Alphabetic T9 searches the CDB for the most recently entered word. Alphabetic T9 requires this information for next-word prediction.
  • a CDB's size is indicated by the value of T9AWCdblnfo.wDataSize.
  • the wDataSize field indicates the total size of T9AWCdblnfo. This includes the data area, where CDB data are stored, several related variables used by T9, and any structure- padding bytes added by the compiler environment.
  • T9's Function API it is not necessary to set the value of T9AWCdblnfo.wDataSize directly. Instead, the size of the CDB data area is provided as an argument to the function T9AWCdbActivate. While handling the function, T9 sets the value of T9AWCdblnfo.wDataSize.
  • CDB area As large wanted, but it must be at least T9MINCDBDATABYTES bytes. It is recommended, however, that the CDB be 1800 * T9SYMBOLWIDTH bytes in size.
  • Alphabetic T9 ensures the integrity of the database by:
  • Alphabetic T9 detects a problem, it resets the CDB, which deletes all CDB data. This process occurs without any action by the integration layer, and Alphabetic T9 does not notify the integration layer that the CDB has been reset.
  • the integration layer can explicitly reset the CDB by calling T9AWCdbReset. Under most circumstances, the integration layer does not need to call this function.
  • the integration layer must write data to the database. Also, one may wish to have the integration layer write data to the CDB if it is desired to monitor what is written to the database or maintain a shadow copy of the CDB in non-volatile storage. [1079] The integration layer informs Alphabetic T9 that it writes data by calling
  • Alphabetic T9 requests that the integration layer write data by calling T9REQCDBWRITE. If it is no longer necessary for the integration layer to write data to the CDB, the integration layer calls
  • T9AWCIrCdbWriteByOEM to indicate that Alphabetic T9 can write data directly.
  • T9 When CDB operations are activated, T9 by default provides predicted words, i.e. words the user may want to enter, based on the words the user has already entered. Next-word prediction is available in both Ambiguous and Multitap text-entry modes.
  • Alphabetic T9 places predicted words in the selection list when the word the user has just entered is found in the CDB as the first part of one or more word pairs. Whatever word appears in the CDB after each instance of the word the user has just entered is provided as a predicted word.
  • Alphabetic T9 When CDB operations are activated, Alphabetic T9 by default places in the selection list completed CDB words that match the active sequence (and contain additional characters) if the word immediately before the active word is in the CDB immediately before the completed word(s). One can disable this functionality if one want to use only next-word prediction, and not CDB word completion, in an Alphabetic T9 implementation.
  • the integration layer To disable CDB word completion, the integration layer calls T9AWCIrCdbCompletion.
  • T ⁇ AWSetCdbCompletion To re-enable CDB word completion, the integration layer calls T ⁇ AWSetCdbCompletion.
  • the integration layer may need to handle the following T9 request:
  • T9REQCDBWRITE Requests that the integration layer write data to the CDB. T9 submits this request only if the integration layer informs T9 that it, and not T9, writes data to the CDB.
  • the integration layer should copy the CDB data to persistent memory when it terminates Alphabetic T9 if the database has been modified during the T9 session.
  • T9 increments the value of T9AWCdblnfo.wUpdateCounter whenever it modifies the database.
  • the integration layer can determine whether the database has been modified by comparing the value of wUpdateCounter after the session to its value before the session. If the value is different, the integration layer must copy the updated CDB data to persistent memory. Note that it is likely that T9 modifies the CDB during every session.
  • Alphabetic T9 CDB operations consist of the following tasks: • Adding data to a CDB.
  • Alphabetic T9 automatically adds data to the CDB. Note that if the CDB is stored in a memory area that T9 cannot write to, the integration layer must write data to the CDB.
  • Alphabetic T9 automatically retrieves data from the CDB.
  • Alphabetic T9 does not permit users or the integration layer to delete words from the database. Instead, Alphabetic T9 automatically begins deleting the oldest words in the database when it is nearly full. This removal process is referred to as garbage collection, and it occurs without any action by the user or integration layer.
  • saved context data are used to return a prediction of the next word upon pressing the space, and to filter the word completions after entering key strokes.
  • This allows a user to reduce the number of keystrokes by quickly retrieving words that are correctly predicted based on the previous word or words.
  • This completion feature is presently implemented by saving user entered text in a Context DataBase (CDB), and returning those words that match context and keystrokes.
  • NWP saves the recently entered user text and uses that text to predict the next word that the user enters. For example, if the user has typed the phrases 'hello Leslie,' hello Inger,' and 'Hello Helena' in the recent past, when the user types and accepts the word 'hello' by hitting space, the system suggests:
  • the system uses context to prioritize completions presented to the user.
  • the selection list presented to the user is: i h
  • selection list presented to the user is: he if id ie ge gf
  • CDB context database
  • CDB objects are of the length of the active key sequence, the objects appear at the top of the selection list.
  • pFieldlnfo->nWordl_en length of active key sequence
  • pFieldlnfo->nComplI_en length of completion.
  • T9EVTCDB T ⁇ CTRLCDBFILLCONTEXTBUFFER buffer: pEvent->data.sCDBData.psBuf buffer length pEvent->data.sCDBData.nBufl_en
  • T9EVTCDB T ⁇ CTRLCDBGETWORDPREDICTION
  • T9STATUS T9FARCALL T9AW_SaveAndAddToCdb(T9AWFieldlnfo * pAWFieldlnfo) Adds Saves word to context buffer and add to context database. This function is called only after a space has been entered.
  • T9U16 wUpdateCounter /* Count incremented each time user database modified */ T9U16 wSymbolClass; /* T9 enum value indicating symbol table mapping for CDB
  • T9U16 wSavedOffset /* pointer to last accessed position in database */ T9U32 dwOffsetSaver; /* identifier for thread that last saved offset.
  • the system word builder passes a context buffer. Using the context buffer the CDB retrieves context matches in order of recency.
  • a context buffer is maintained.
  • the context buffer is updated on the pressing of space key and is cleared with any action that tends to lose context, such as cursoring and clearing.
  • this is attached to the flushword function of a separate confirm function.
  • the NWP feature is active if: a) the compile includes the code for this feature; and b) the field info member pFieldlnfo->pCdbinfo points to valid memory.
  • FD100 T9 core saves in the CDB every recent word that was used. The number of words saved depends on the size allocated by the OEM to the CDB.
  • FD200 T9 ambiguous and MT modes return next word predictions after a space if there is an active word or the previous key hit is a T9 number key.
  • FD300 T9 ambiguous and MT modes return next word predictions after right arrow and space if there is an active word before the right arrow is pressed.
  • T9 After cursoring off a word, and moving around the buffer, T9 does not present a prediction after space is hit.
  • T9 presents a prediction if a prediction is active and the user hits space to clear the prediction, hits clear again to clear the space, and then hits space again.
  • FD800 No CDB predictions/completions are delivered across sentence punctuation.
  • Sentence punctuation is defined as trailing punctuation on a non-emoticon. See FD1200 for definition of emoticon.
  • FD1000 There is no aging of the CDB; the least recent word is replaced by the most recent word entered.
  • FD1100 Context bigrams are recorded in the CDB on pressing space if there is an active word, or the previously hit key is a T9 number key. If the user cursors off a word, context is broken in the CDB.
  • Use Case 1 ) User and has recently entered the bigrams 'my money,' 'my time,' and 'my marriage,', and the unigram 'mobetterblues' in order written here.
  • Use Case 1 ) User and has recently entered the bigrams, 'my money,' 'my money,' 'my marriage' in order written here.
  • CDB is language independent.
  • RUDB processes around reordering of non-completed words remain unchanged.
  • Context predictions are not delivered after clearing a current word, but are delivered as the user begins typing again.
  • user actions or inputs can affect the automatic changing of the device's state based on context.
  • the system might use context to change a mobile telephone from 'ring' to 'vibrate', during the time that the calendar shows that the user is in a meeting.
  • Another embodiment uses location context to increase the mobile telephone volume when the user is outside or when the telephone detects high levels of background noise.
  • the system learns the user habits. For example, based on the learned user action, the system is able to offer services to the user that the user may not be aware of.
  • word prediction is based on the previous word context (bigram context), but might also use the previous 'n' words (trigram context, etc).
  • FIGURE 5 shows a sequence 500 to illustrate an example of a different process aspect of this disclosure.
  • this sequence operates a handheld computing device to automatically detect cues describing the device's environment and user actions performed with the device, learn which cues and cue combinations are relevant to predict user actions, and then in response to occurrence of the relevant cues, predictively implementing the appropriate user action or configuring the device in anticipation of user action.
  • FIGURE 5 The example of FIGURE 5 is described in the specific context of the handheld computing device 101 of FIGURE 2 as described above.
  • Such device 101 may, optionally, include telephone features according to FIGURE 1.
  • This particular example is used for ease of explanation, and to provide an adequate foundation for discussing a wide range of features of the sequence 500, without any intended limitation.
  • step 502 the device 101 detects all available cues. This involves the CPU 100 evaluating the state of the sensors 55, operating system 106, application programs 112/114, and processing this input as needed to determine the nature and extent of the presently occurring cues.
  • Some examples of cues include:
  • the application context may refer to which application the user currently has opened.
  • the application context includes a recognition that user is currently operating an instant messaging application such as AOL Instant Messenger (AIM).
  • AIM AOL Instant Messenger
  • the machine 101's computing environment such as the processing load, presence or absence of detachable peripheral devices, network capacity, connectivity, computing costs, etc.
  • Cues may concern individual pieces of information, or combined items of data. For example, one cue may specify a given situation of the device relative to the user, such as “device in-hand” versus “device in-pocket” according to various combinations of light, temperature, and accelerometer outputs.
  • the software state of the device 101 by virtue of the user having performed a given sequence of menu or other operations within an application, or having opened or closed an entire application.
  • the message recipient, and/or the message sender which can indicate preferences for things like a shared lingo, a frequent topic of conversation, a preferred channel of communications, and/or interrelationships with other data stored on the device such as sets of pictures or music tracks.
  • step 503 the CPU 100 monitors and analyzes operation of the device 101 to identify any user actions that can be associated with the cues from step 502.
  • step 504 learns which if any cues and combinations of cues are relevant predictors of which of user actions.
  • "User actions” include virtually any action that the user, by operating the user input 54, can instruct the device 101 to perform, such as configuring the device and operating its application programs.
  • some examples as to configuring the device include switching to hands-free or voice navigation driving mode, causing associated cell phone to enter a silent or vibrate mode, adjusting display brightness, etc.
  • step 503 may be performed on a repeating basis, such as continuously, periodically, on a non-periodic schedule, etc.
  • step 503 detects user actions and maintains a running list of user actions.
  • the user actions are stored in actions 125 (FIGURE 2).
  • the CPU 100 After initially detecting and cataloguing the user actions, or concurrently with this step, the CPU 100 analyzes the user actions 125 against the detected cues 123 (from 502) to learn which (if any) of the cues are relevant predictors for which of the user actions. In other words, this step identifies each logically connected pair from 123/125, where occurrence of a state cue is a reliable indicator of a certain user action. Some cues may be meaningless, in that they are not relevant to predict any following user actions. Likewise, some user actions might never be preceded by any predictable context cue.
  • the mapping 124 may be constructed in various ways.
  • the mapping 124 may be prepared by using a neural network or by constructing an associative array.
  • the mapping 124 may be prepared by empirical data, for example, by randomly assigning cues to user actions and continually refining this model by discarding unworkable cue-action links and reinforcing proven cue-action links.
  • mapping 124 Another example of how to prepare the mapping 124 is specifically described below, under the heading "One Example of Mapping.”
  • the CPU 100 plans one or more machine-executed actions to (1) configure the device in anticipation of this action, or (2) cause the device carry out the next user action, (3) or to priority certain interpretations of inherently ambiguous user-entered text.
  • step 504 may be performed on a repeating basis, such as continuously, periodically, on a non-periodic schedule, etc. in order to maintain a sufficiently refined list of predictive actions.
  • the device 101 may promote “lunch” while in the evening the device 101 promotes "dinner” or "supper.”
  • step 503 may propose user action that is logically appropriate for the cues even if such action has never been taken. Even more particularly, step 503 may propose that ambiguously entered user text be prioritized according to an context indicated by certain cues. This user action, as discussed below, is carried out in step 508b. As a further example of the foregoing alternative embodiment, step 503 may propose automatically favoring a user's screen name as the best interpretation of a user-entered key sequences whenever the user is operating an instant messaging application.
  • step 503 proposes automatically favoring text (such as zip code, address, city, state, etc.) consistent with a cue of detected geographic location.
  • step 503 may propose automatically favoring interpretations of user entered text consistent with the time of day, day of week, month, or year in order to remain consistent with a cue of detected time.
  • step 503 may propose automatically favoring interpretations of user entered text consistent with a form field in response to a cue indicating that a text entry cursor lies in a given field.
  • step 506 monitors for occurrence of one of the cues or cue combinations that was found to constitute a relevant predictor of a user action and mapped to that action in step 503. In the absence of any such cues, step 506 repeats (506c) to wait for the next relevant cue. Whenever the CPU 100 finds that a relevant predictor has occurred (506a, 506b), the CPU 100 performs the correlated (via 124) machine-executed action (125/504) in steps 508a or 508b. These actions, planned in step 504 and discussed above, include anticipating the next user action (508a) and prioritizing inherently ambiguous user text entry (508b).
  • the following examples illustrate some examples of steps 506, 508a, and namely, detecting a relevant predictor and performing a correlated predictive action.
  • the device 101 detects (506) street noise
  • the CPU 100 automatically switches (508a) to hands-free or voice navigation driving mode.
  • the device 101 detects (506) arrival of a meeting time recorded in an integrated calendar program
  • the device 101 automatically puts (508a) an associated cell phone into a silent or vibrate mode.
  • the CPU 100 automatically configures (508a) the menu to include or highlight an expected follow-up menu entry.
  • the device 101 when the device 101 finds (506) that the user has performed a cut function inside an application program, the device 101 may automatically restructure, prune, or otherwise modify the "edit" menu (508a) to highlight the "paste" action; similar activity may be conducted for search/replace or other related menu entries.
  • the device 101 detects (506) that the user has entered a settings mode of the device 101 and chosen an input method or language, the device 101 anticipates (508a) the user opening his/her favorite messaging application by automatically configuring (508a) the device 101 to bypass the normal menu tree and open the messaging application automatically, or to present a prominent option in a settings window or elsewhere along with the anticipated user action.
  • the device 101 may also present an option "display regular menu” in case the anticipated action turns out to be incorrect.
  • the device 101 anticipates (508a) that the photo editing tool palette needs to include the "red eye” removal tool; or, further, by making red eye detection and selection the first step when the user next launches the photo editing application with that picture.
  • the device 101 detects (506) user completion of a given application program, the device 101 automatically opens (508a) another application program. For example, the device 101 may have learned that the user always opens a "note pad" application after completing a phone call.
  • the device 101 responds to user completion of a phone call to his psychic (506) by initiating (508a) an instant message to the user's stockbroker.
  • the following illustrates some examples of steps 506/508b, and namely, detecting a relevant predictor and performing a correlated predictive action relating to disambiguation.
  • the device 101 when the device 101 detects (506) that the user has an instant messaging application open, the device 101 automatically promotes (506b) the user's screen name as the best interpretation of a user-entered key sequence.
  • the device 101 when the device 101 detects (506a) a geographic location obtained from an integrated or remotely coupled GPS unit, the device 101 favors (506b) interpretations of user entered text consistent with that geographic location such as the city, state, or a nearby street address. More particularly, when the device 110, using data from an integrated or remotely coupled GPS unit, senses a geographic location near Queen Anne Ave N and W Galer St., Seattle, WA, the device 101 automatically promotes 98119 as the best interpretation of a user-entered key sequence in a zip code field.
  • the device 101 when the device 101 , using data from an integrated or remotely coupled clock, senses (506a) the date and/or time, the device 101 automatically promotes (508b) words consistent with the time of day, time of year, month, etc. For example, if the user enters "Let's go out for" in an instant messaging application, the machine 101 may promote or suggest “lunch” when the time is near noon. As another example, if the user types "I want to go", the machine 101 may promote or favor "skiing” in the winter and “swimming” in the summer. As another example, when device 101 detects (506a) that the cursor, in a currently open application, is located in certain form field, the disambiguation operation favors (508b) interpretations of user entered text consistent with the field.
  • mapping operation of step 503 may be implemented in various ways.
  • One example is discussed in U.S. Provisional Application 60/734,840 filed 11-9-2005 in the name of Guarav Tandon, which is incorporated herein by reference.
  • Other examples are discussed in U.S. Patent Application No. 11/392,186 filed 3-28-2006 in the name of Guarav Tandon, which is also incorporated herein by reference.
  • step 503 The following provides an additional, more specific example of the mapping operation of step 503 (FIGURE 5).
  • a learning sequence randomly chooses instances on the basis of matching target values.
  • the learning sequence generates candidate rules by randomly selecting matching attribute values, the motivation being that matching attributes would help capture the correlation between the various attributes.
  • the learning sequence then removes rules that are redundant, with the more general rules being preferred over the specialized ones.
  • the learning sequence then updates rules over the entire initial instance space such that the consequent has last m values for the target attribute given all conditions in the antecedent are true (m being an integer).
  • the learning sequence further associates every rule with a weight.
  • the weight is initialized to one. In alternate embodiments, other initial values may be practiced.
  • the learning sequence checks all rules against it.
  • the rules whose antecedents are true for the current instance are considered to be eligible to vote on the target value.
  • Each conformed rule votes for its most frequent antecedent value and the number of votes is same as the weight for the rule.
  • initially, all conformed rules have equal vote (since they all have the same initial weight, e.g., one).
  • the learning sequence then aggregates the votes and predicts the target value.
  • the learning sequence predicts the target value with the majority vote. If no rule antecedent is true, a default majority value is predicted from the last m outcomes.
  • the learning sequence decreases the weight if the local pre-diction by a participating rule is incorrect, irrespective of the correctness of the global outcome. In various embodiments, the decrement is by half. In various embodiments, when the local prediction is correct but the global outcome is incorrect, the learning sequence measures the vote deficit for the actual prediction. After that, the learning sequence increases the weights for all the rules that had the correct local prediction in the event the global outcome failed to predict correctly. In various embodiments, the weights are increased equally. This boosts the vote for the correct target value.
  • the learning sequence increments its weight conservatively.
  • the technique conjectures that this reward raises the confidence (weight) of the rule for future predictions.
  • 0.1 is employed as the reward value.
  • different reward values may be employed.
  • Liberally rewarding the rules eventually may lead to a drop in the performance, so this parameter is generally selected carefully.
  • experiments appear to suggest a small linear increase in weight performs much better than exponential increase.
  • the weight of any rule falls below a user-defined threshold, the rule is removed from the ruleset.
  • the learning sequence updates the rule by replacing the oldest target value with the current one. Further, in various embodiments, if the prediction is incorrect, the learning sequence updates the instance space by replacing the oldest instance with the current one. New rules are generated the same way as the initial rules, and redundancy is removed. New rules are each assigned a weight, in various embodiments, the weight of one. The learning sequence then uses this updated model for subsequent instance. The model is thus updated incrementally.
  • any illustrative logical blocks, modules, circuits, and process steps described herein may be implemented as electronic hardware, computer software, or combinations of both.
  • various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
  • DSP digital signal processor
  • ASIC application specific integrated circuit
  • FPGA field programmable gate array
  • a general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine.
  • a processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.
  • a software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, DVD, or any other form of storage medium known in the art.
  • An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium.
  • the storage medium may be integral to the processor.
  • the processor and the storage medium may reside in an ASIC.

Abstract

L'invention concerne une séquence d'opérations, destinée à un dispositif informatique portatif, pour gérer le dispositif et détecter automatiquement des signaux d'avertissement décrivant l'environnement du dispositif et les actions de l'utilisateur effectuées sur le dispositif, apprendre les signaux d'avertissement et les combinaisons de signaux d'avertissement pertinents pour prédire les actions de l'utilisateur, puis, en réponse à l'apparition des signaux d'avertissement pertinents, mettre en œuvre de manière prédictive l'action appropriée de l'utilisateur ou configurer le dispositif en anticipant l'action de l'utilisateur.
EP07760919.6A 2006-04-21 2007-04-19 Prédiction contextuelle de mots d'utilisateur et d'actions d'utilisateur Active EP2013761B1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US11/379,747 US8938688B2 (en) 1998-12-04 2006-04-21 Contextual prediction of user words and user actions
PCT/US2007/066974 WO2007124364A2 (fr) 2006-04-21 2007-04-19 Prédiction contextuelle de mots d'utilisateur et d'actions d'utilisateur

Publications (3)

Publication Number Publication Date
EP2013761A2 true EP2013761A2 (fr) 2009-01-14
EP2013761A4 EP2013761A4 (fr) 2012-01-04
EP2013761B1 EP2013761B1 (fr) 2016-11-02

Family

ID=38625738

Family Applications (1)

Application Number Title Priority Date Filing Date
EP07760919.6A Active EP2013761B1 (fr) 2006-04-21 2007-04-19 Prédiction contextuelle de mots d'utilisateur et d'actions d'utilisateur

Country Status (4)

Country Link
US (2) US8938688B2 (fr)
EP (1) EP2013761B1 (fr)
CN (2) CN105528143A (fr)
WO (1) WO2007124364A2 (fr)

Families Citing this family (268)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7286115B2 (en) 2000-05-26 2007-10-23 Tegic Communications, Inc. Directional input system with automatic correction
US7030863B2 (en) 2000-05-26 2006-04-18 America Online, Incorporated Virtual keyboard system with automatic correction
US7610194B2 (en) * 2002-07-18 2009-10-27 Tegic Communications, Inc. Dynamic database reordering system
US7821503B2 (en) 2003-04-09 2010-10-26 Tegic Communications, Inc. Touch screen and graphical user interface
US7750891B2 (en) 2003-04-09 2010-07-06 Tegic Communications, Inc. Selective input system based on tracking of motion parameters of an input device
AU5299700A (en) 1999-05-27 2000-12-18 America Online, Inc. Keyboard system with automatic correction
US7620683B2 (en) * 2001-05-18 2009-11-17 Kabushiki Kaisha Square Enix Terminal device, information viewing method, information viewing method of information server system, and recording medium
JP5087273B2 (ja) * 2003-06-18 2012-12-05 ザイ コーポレイション オブ カナダ インコーポレイテッド 設定可能な情報特定システム及び方法
US8374846B2 (en) 2005-05-18 2013-02-12 Neuer Wall Treuhand Gmbh Text input device and method
US9606634B2 (en) * 2005-05-18 2017-03-28 Nokia Technologies Oy Device incorporating improved text input mechanism
US8036878B2 (en) 2005-05-18 2011-10-11 Never Wall Treuhand GmbH Device incorporating improved text input mechanism
US8117540B2 (en) * 2005-05-18 2012-02-14 Neuer Wall Treuhand Gmbh Method and device incorporating improved text input mechanism
US8677377B2 (en) 2005-09-08 2014-03-18 Apple Inc. Method and apparatus for building an intelligent automated assistant
US8504606B2 (en) * 2005-11-09 2013-08-06 Tegic Communications Learner for resource constrained devices
US8065135B2 (en) 2006-04-06 2011-11-22 Research In Motion Limited Handheld electronic device and method for employing contextual data for disambiguation of text input
US7477165B2 (en) 2006-04-06 2009-01-13 Research In Motion Limited Handheld electronic device and method for learning contextual data during disambiguation of text input
US8166418B2 (en) * 2006-05-26 2012-04-24 Zi Corporation Of Canada, Inc. Device and method of conveying meaning
US8374862B2 (en) * 2006-08-30 2013-02-12 Research In Motion Limited Method, software and device for uniquely identifying a desired contact in a contacts database based on a single utterance
US9318108B2 (en) 2010-01-18 2016-04-19 Apple Inc. Intelligent automated assistant
FR2910143B1 (fr) * 2006-12-19 2009-04-03 Eastman Kodak Co Procede pour predire automatiquement des mots dans un texte associe a un message multimedia
US8074172B2 (en) * 2007-01-05 2011-12-06 Apple Inc. Method, system, and graphical user interface for providing word recommendations
US20080168478A1 (en) 2007-01-07 2008-07-10 Andrew Platzer Application Programming Interfaces for Scrolling
US20080168402A1 (en) 2007-01-07 2008-07-10 Christopher Blumenberg Application Programming Interfaces for Gesture Operations
US8201087B2 (en) 2007-02-01 2012-06-12 Tegic Communications, Inc. Spell-check for a keyboard system with automatic correction
US8225203B2 (en) 2007-02-01 2012-07-17 Nuance Communications, Inc. Spell-check for a keyboard system with automatic correction
US8880405B2 (en) 2007-03-07 2014-11-04 Vlingo Corporation Application text entry in a mobile environment using a speech processing facility
US10056077B2 (en) 2007-03-07 2018-08-21 Nuance Communications, Inc. Using speech recognition results based on an unstructured language model with a music system
US8949266B2 (en) 2007-03-07 2015-02-03 Vlingo Corporation Multiple web-based content category searching in mobile search application
US8886545B2 (en) 2007-03-07 2014-11-11 Vlingo Corporation Dealing with switch latency in speech recognition
US8886540B2 (en) 2007-03-07 2014-11-11 Vlingo Corporation Using speech recognition results based on an unstructured language model in a mobile communication facility application
US8838457B2 (en) 2007-03-07 2014-09-16 Vlingo Corporation Using results of unstructured language model based speech recognition to control a system-level function of a mobile communications facility
US8635243B2 (en) 2007-03-07 2014-01-21 Research In Motion Limited Sending a communications header with voice recording to send metadata for use in speech recognition, formatting, and search mobile search application
US8949130B2 (en) 2007-03-07 2015-02-03 Vlingo Corporation Internal and external speech recognition use with a mobile communication facility
US20080244446A1 (en) * 2007-03-29 2008-10-02 Lefevre John Disambiguation of icons and other media in text-based applications
WO2008120033A1 (fr) * 2007-03-29 2008-10-09 Nokia Corporation Prioritisation des mots sur la base du contenu d'une entrée
US8977255B2 (en) 2007-04-03 2015-03-10 Apple Inc. Method and system for operating a multi-function portable electronic device using voice-activation
EP2156325A1 (fr) * 2007-06-18 2010-02-24 France Telecom Procede de saisie d'une information dans un formulaire electronique
US9251137B2 (en) * 2007-06-21 2016-02-02 International Business Machines Corporation Method of text type-ahead
KR20090024423A (ko) * 2007-09-04 2009-03-09 한국전자통신연구원 지능형 시스템의 학습 장치 및 방법
US7974979B2 (en) * 2007-10-05 2011-07-05 Zi Corporation Of Canada, Inc. Inquiry-oriented user input apparatus and method
JP4503069B2 (ja) * 2007-12-27 2010-07-14 シャープ株式会社 文字入力装置、システム、及び文字入力制御方法
US8232973B2 (en) 2008-01-09 2012-07-31 Apple Inc. Method, device, and graphical user interface providing word recommendations for text input
US8316035B2 (en) 2008-01-16 2012-11-20 International Business Machines Corporation Systems and arrangements of text type-ahead
US9552155B2 (en) 2008-02-04 2017-01-24 Microsoft Technology Liecsnsing, LLC Dynamic soft keyboard
US8645827B2 (en) 2008-03-04 2014-02-04 Apple Inc. Touch event model
US8717305B2 (en) 2008-03-04 2014-05-06 Apple Inc. Touch event model for web pages
US8396582B2 (en) 2008-03-08 2013-03-12 Tokyo Electron Limited Method and apparatus for self-learning and self-improving a semiconductor manufacturing tool
US8725667B2 (en) * 2008-03-08 2014-05-13 Tokyo Electron Limited Method and system for detection of tool performance degradation and mismatch
US8190543B2 (en) 2008-03-08 2012-05-29 Tokyo Electron Limited Autonomous biologically based learning tool
US20090249198A1 (en) * 2008-04-01 2009-10-01 Yahoo! Inc. Techniques for input recogniton and completion
WO2009128838A1 (fr) * 2008-04-18 2009-10-22 Tegic Communications, Inc. Levée d'ambiguïté d'icônes et d'autres éléments multimédias dans des applications à base de texte
US8359532B2 (en) * 2008-04-28 2013-01-22 International Business Machines Corporation Text type-ahead
JP4655109B2 (ja) * 2008-05-12 2011-03-23 オムロン株式会社 文字入力用のプログラムおよび文字入力装置ならびに文字入力方法
US10496753B2 (en) * 2010-01-18 2019-12-03 Apple Inc. Automatically adapting user interfaces for hands-free interaction
ATE501478T1 (de) * 2008-06-11 2011-03-15 Exb Asset Man Gmbh Vorrichtung und verfahren mit verbessertem texteingabemechanismus
US8676904B2 (en) 2008-10-02 2014-03-18 Apple Inc. Electronic devices with voice command and contextual data processing capabilities
US20100131447A1 (en) * 2008-11-26 2010-05-27 Nokia Corporation Method, Apparatus and Computer Program Product for Providing an Adaptive Word Completion Mechanism
US20100130236A1 (en) * 2008-11-26 2010-05-27 Nokia Corporation Location assisted word completion
US20100146073A1 (en) * 2008-12-04 2010-06-10 Kerstin Dittmar Mobile data upload to enterprise computer systems
US8786551B2 (en) * 2008-12-11 2014-07-22 Verizon Patent And Licensing Inc. Predictive keypad/keyboard lighting to guide input
US8677236B2 (en) 2008-12-19 2014-03-18 Microsoft Corporation Contact-specific and location-aware lexicon prediction
US8669941B2 (en) 2009-01-05 2014-03-11 Nuance Communications, Inc. Method and apparatus for text entry
US10176162B2 (en) * 2009-02-27 2019-01-08 Blackberry Limited System and method for improved address entry
US8566045B2 (en) 2009-03-16 2013-10-22 Apple Inc. Event recognition
US9684521B2 (en) 2010-01-26 2017-06-20 Apple Inc. Systems having discrete and continuous gesture recognizers
US8285499B2 (en) 2009-03-16 2012-10-09 Apple Inc. Event recognition
US9189472B2 (en) 2009-03-30 2015-11-17 Touchtype Limited System and method for inputting text into small screen devices
US9424246B2 (en) 2009-03-30 2016-08-23 Touchtype Ltd. System and method for inputting text into electronic devices
GB0917753D0 (en) 2009-10-09 2009-11-25 Touchtype Ltd System and method for inputting text into electronic devices
GB0905457D0 (en) 2009-03-30 2009-05-13 Touchtype Ltd System and method for inputting text into electronic devices
US10191654B2 (en) 2009-03-30 2019-01-29 Touchtype Limited System and method for inputting text into electronic devices
KR101542136B1 (ko) * 2009-03-31 2015-08-05 삼성전자 주식회사 문자 메시지 작성 방법 및 이를 이용한 휴대 단말기
US8850472B2 (en) * 2009-04-01 2014-09-30 Nuance Communications, Inc. Method and apparatus for customizing user experience
US10706373B2 (en) 2011-06-03 2020-07-07 Apple Inc. Performing actions associated with task items that represent tasks to perform
KR101562792B1 (ko) * 2009-06-10 2015-10-23 삼성전자주식회사 목표 예측 인터페이스 제공 장치 및 그 방법
US20100317371A1 (en) * 2009-06-12 2010-12-16 Westerinen William J Context-based interaction model for mobile devices
US20100332215A1 (en) * 2009-06-26 2010-12-30 Nokia Corporation Method and apparatus for converting text input
US20110042102A1 (en) * 2009-08-18 2011-02-24 Frank's International, Inc. Method of and kit for installing a centralizer on a pipe segment
US9110515B2 (en) 2009-08-19 2015-08-18 Nuance Communications, Inc. Method and apparatus for text input
US9223590B2 (en) * 2010-01-06 2015-12-29 Apple Inc. System and method for issuing commands to applications based on contextual information
US8121618B2 (en) 2009-10-28 2012-02-21 Digimarc Corporation Intuitive computing methods and systems
US11416214B2 (en) 2009-12-23 2022-08-16 Google Llc Multi-modal input on an electronic device
EP2339576B1 (fr) 2009-12-23 2019-08-07 Google LLC Entrée multimodale sur un dispositif électronique
US10553209B2 (en) 2010-01-18 2020-02-04 Apple Inc. Systems and methods for hands-free notification summaries
US10705794B2 (en) 2010-01-18 2020-07-07 Apple Inc. Automatically adapting user interfaces for hands-free interaction
US10679605B2 (en) 2010-01-18 2020-06-09 Apple Inc. Hands-free list-reading by intelligent automated assistant
US10276170B2 (en) 2010-01-18 2019-04-30 Apple Inc. Intelligent automated assistant
US20120303452A1 (en) * 2010-02-03 2012-11-29 Nokia Corporation Method and Apparatus for Providing Context Attributes and Informational Links for Media Data
US8682667B2 (en) 2010-02-25 2014-03-25 Apple Inc. User profiling for selecting user specific voice input processing information
US8552999B2 (en) 2010-06-14 2013-10-08 Apple Inc. Control selection approximation
US8473289B2 (en) * 2010-08-06 2013-06-25 Google Inc. Disambiguating input based on context
CN102375667B (zh) * 2010-08-24 2015-11-25 腾讯科技（深圳）有限公司 定位输入焦点的方法和装置
US8448089B2 (en) 2010-10-26 2013-05-21 Microsoft Corporation Context-aware user input prediction
US10026058B2 (en) * 2010-10-29 2018-07-17 Microsoft Technology Licensing, Llc Enterprise resource planning oriented context-aware environment
US20120137254A1 (en) * 2010-11-29 2012-05-31 Dynavox Systems Llc Context-aware augmented communication
US8352245B1 (en) 2010-12-30 2013-01-08 Google Inc. Adjusting language models
KR101828273B1 (ko) * 2011-01-04 2018-02-14 삼성전자주식회사 결합기반의 음성명령 인식 장치 및 그 방법
WO2012098428A1 (fr) * 2011-01-18 2012-07-26 Nokia Corporation Exécution de tâche
US8296142B2 (en) 2011-01-21 2012-10-23 Google Inc. Speech recognition using dock context
US9439226B2 (en) * 2011-03-15 2016-09-06 Nokia Technologies Oy Method and apparatus for initiating radio connections
US9262612B2 (en) 2011-03-21 2016-02-16 Apple Inc. Device access using voice authentication
EP2511794A1 (fr) * 2011-04-15 2012-10-17 Doro AB Dispositif électronique portable doté d'une temporisation d'entrée par touche multifonction configurable par l'utilisateur
US10057736B2 (en) 2011-06-03 2018-08-21 Apple Inc. Active transport based notifications
CN102269534B (zh) 2011-07-25 2012-11-28 天津空中代码工程应用软件开发有限公司 一种旋流式热导管
CN102891916B (zh) * 2011-07-18 2016-01-20 中兴通讯股份有限公司 一种预测用户操作的方法及移动终端
US8707157B1 (en) * 2011-08-19 2014-04-22 Intuit Inc. System and method for pre-populating forms using statistical analysis
US20130135218A1 (en) * 2011-11-30 2013-05-30 Arbitron Inc. Tactile and gestational identification and linking to media consumption
US9519909B2 (en) 2012-03-01 2016-12-13 The Nielsen Company (Us), Llc Methods and apparatus to identify users of handheld computing devices
US10134385B2 (en) 2012-03-02 2018-11-20 Apple Inc. Systems and methods for name pronunciation
CN102629160B (zh) * 2012-03-16 2016-08-03 华为终端有限公司 一种输入法、输入装置及终端
US8473975B1 (en) 2012-04-16 2013-06-25 The Nielsen Company (Us), Llc Methods and apparatus to detect user attentiveness to handheld computing devices
US9008688B2 (en) * 2012-05-07 2015-04-14 Qualcomm Incorporated Calendar matching of inferred contexts and label propagation
US10417037B2 (en) 2012-05-15 2019-09-17 Apple Inc. Systems and methods for integrating third party services with a digital assistant
CN102722410B (zh) 2012-05-25 2015-07-29 华为终端有限公司 执行程序的方法、服务器、移动终端及系统
KR20140011073A (ko) * 2012-07-17 2014-01-28 삼성전자주식회사 텍스트 추천 방법 및 장치
US20140025367A1 (en) * 2012-07-18 2014-01-23 Htc Corporation Predictive text engine systems and related methods
US9239824B2 (en) 2012-07-27 2016-01-19 Semantic Compaction Systems, Inc. Apparatus, method and computer readable medium for a multifunctional interactive dictionary database for referencing polysemous symbol sequences
US8843845B2 (en) 2012-10-16 2014-09-23 Google Inc. Multi-gesture text input prediction
US8965754B2 (en) 2012-11-20 2015-02-24 International Business Machines Corporation Text prediction using environment hints
US9244905B2 (en) * 2012-12-06 2016-01-26 Microsoft Technology Licensing, Llc Communication context based predictive-text suggestion
US8977555B2 (en) * 2012-12-20 2015-03-10 Amazon Technologies, Inc. Identification of utterance subjects
US8832589B2 (en) 2013-01-15 2014-09-09 Google Inc. Touch keyboard using language and spatial models
JP2016508007A (ja) 2013-02-07 2016-03-10 アップル インコーポレイテッド デジタルアシスタントのためのボイストリガ
US9223297B2 (en) 2013-02-28 2015-12-29 The Nielsen Company (Us), Llc Systems and methods for identifying a user of an electronic device
US9507862B2 (en) * 2013-03-14 2016-11-29 Microsoft Technology Licensing, Llc Chronology based content processing
US10652394B2 (en) 2013-03-14 2020-05-12 Apple Inc. System and method for processing voicemail
US10748529B1 (en) 2013-03-15 2020-08-18 Apple Inc. Voice activated device for use with a voice-based digital assistant
US9672818B2 (en) 2013-04-18 2017-06-06 Nuance Communications, Inc. Updating population language models based on changes made by user clusters
US9465985B2 (en) 2013-06-09 2016-10-11 Apple Inc. Managing real-time handwriting recognition
CN110442699A (zh) 2013-06-09 2019-11-12 苹果公司 操作数字助理的方法、计算机可读介质、电子设备和系统
US10176167B2 (en) 2013-06-09 2019-01-08 Apple Inc. System and method for inferring user intent from speech inputs
US9733716B2 (en) 2013-06-09 2017-08-15 Apple Inc. Proxy gesture recognizer
US8631325B1 (en) 2013-08-09 2014-01-14 Zoomdata, Inc. Real-time data visualization of streaming data
CN104423793A (zh) * 2013-09-11 2015-03-18 联想(北京)有限公司 一种信息处理方法及电子设备
US9558452B2 (en) 2013-11-08 2017-01-31 Microsoft Technology Licensing, Llc Hierarchical statistical model for behavior prediction and classification
US10296160B2 (en) 2013-12-06 2019-05-21 Apple Inc. Method for extracting salient dialog usage from live data
US20150169537A1 (en) * 2013-12-13 2015-06-18 Nuance Communications, Inc. Using statistical language models to improve text input
US9311292B2 (en) * 2014-01-20 2016-04-12 Lenovo (Singapore) Pte. Ltd. Automatic message presentation based on past messages
US9311639B2 (en) 2014-02-11 2016-04-12 Digimarc Corporation Methods, apparatus and arrangements for device to device communication
US9842592B2 (en) 2014-02-12 2017-12-12 Google Inc. Language models using non-linguistic context
US9412365B2 (en) 2014-03-24 2016-08-09 Google Inc. Enhanced maximum entropy models
CN104951219B (zh) * 2014-03-25 2018-06-15 华为技术有限公司 一种移动终端文本输入的方法及移动终端
KR102282487B1 (ko) 2014-05-08 2021-07-26 삼성전자주식회사 애플리케이션 실행 장치 및 방법
US9715875B2 (en) 2014-05-30 2017-07-25 Apple Inc. Reducing the need for manual start/end-pointing and trigger phrases
US9633004B2 (en) 2014-05-30 2017-04-25 Apple Inc. Better resolution when referencing to concepts
US10170123B2 (en) 2014-05-30 2019-01-01 Apple Inc. Intelligent assistant for home automation
US9430463B2 (en) 2014-05-30 2016-08-30 Apple Inc. Exemplar-based natural language processing
US9966065B2 (en) 2014-05-30 2018-05-08 Apple Inc. Multi-command single utterance input method
US9338493B2 (en) 2014-06-30 2016-05-10 Apple Inc. Intelligent automated assistant for TV user interactions
US10074360B2 (en) 2014-09-30 2018-09-11 Apple Inc. Providing an indication of the suitability of speech recognition
US9668121B2 (en) 2014-09-30 2017-05-30 Apple Inc. Social reminders
US10127911B2 (en) 2014-09-30 2018-11-13 Apple Inc. Speaker identification and unsupervised speaker adaptation techniques
US10587541B2 (en) * 2014-12-02 2020-03-10 Facebook, Inc. Device, method, and graphical user interface for lightweight messaging
US10924444B2 (en) * 2014-12-02 2021-02-16 Facebook, Inc. Device, method, and graphical user interface for managing customer relationships using a lightweight messaging platform
CN104484057B (zh) * 2014-12-04 2018-04-13 百度在线网络技术（北京）有限公司 联想结果的提供方法和装置
US9251276B1 (en) 2015-02-27 2016-02-02 Zoomdata, Inc. Prioritization of retrieval and/or processing of data
US9817871B2 (en) 2015-02-27 2017-11-14 Zoomdata, Inc. Prioritized retrieval and/or processing of data via query selection
US10152299B2 (en) 2015-03-06 2018-12-11 Apple Inc. Reducing response latency of intelligent automated assistants
US9886953B2 (en) 2015-03-08 2018-02-06 Apple Inc. Virtual assistant activation
US9721566B2 (en) 2015-03-08 2017-08-01 Apple Inc. Competing devices responding to voice triggers
US10134394B2 (en) 2015-03-20 2018-11-20 Google Llc Speech recognition using log-linear model
US9389909B1 (en) 2015-04-28 2016-07-12 Zoomdata, Inc. Prioritized execution of plans for obtaining and/or processing data
EP3093759B1 (fr) 2015-05-11 2019-12-04 Samsung Electronics Co., Ltd. Dispositif électronique et procédé de gestion d'applications sur un tel dispositif
US9596204B2 (en) 2015-05-15 2017-03-14 Siemens Aktiengesellschaft Determination of a navigational text candidate
US10460227B2 (en) 2015-05-15 2019-10-29 Apple Inc. Virtual assistant in a communication session
US10200824B2 (en) 2015-05-27 2019-02-05 Apple Inc. Systems and methods for proactively identifying and surfacing relevant content on a touch-sensitive device
US10083688B2 (en) 2015-05-27 2018-09-25 Apple Inc. Device voice control for selecting a displayed affordance
CN106293114B (zh) * 2015-06-02 2019-03-29 阿里巴巴集团控股有限公司 预测用户待输入词的方法及装置
US9578173B2 (en) 2015-06-05 2017-02-21 Apple Inc. Virtual assistant aided communication with 3rd party service in a communication session
EP3106985B1 (fr) 2015-06-16 2020-05-06 Huawei Technologies Co., Ltd. Procédé et appareil permettant de classer des activités virtuelles d'utilisateurs mobiles
US20160378747A1 (en) 2015-06-29 2016-12-29 Apple Inc. Virtual assistant for media playback
US10504029B2 (en) 2015-06-30 2019-12-10 Microsoft Technology Licensing, Llc Personalized predictive models
US10055110B2 (en) * 2015-07-27 2018-08-21 Oracle International Corporation Simulating a user interface to submit data received from a device
US10331312B2 (en) 2015-09-08 2019-06-25 Apple Inc. Intelligent automated assistant in a media environment
US10740384B2 (en) 2015-09-08 2020-08-11 Apple Inc. Intelligent automated assistant for media search and playback
US10747498B2 (en) 2015-09-08 2020-08-18 Apple Inc. Zero latency digital assistant
US10671428B2 (en) 2015-09-08 2020-06-02 Apple Inc. Distributed personal assistant
US9665567B2 (en) * 2015-09-21 2017-05-30 International Business Machines Corporation Suggesting emoji characters based on current contextual emotional state of user
US10691473B2 (en) 2015-11-06 2020-06-23 Apple Inc. Intelligent automated assistant in a messaging environment
US10956666B2 (en) 2015-11-09 2021-03-23 Apple Inc. Unconventional virtual assistant interactions
CN105404401A (zh) * 2015-11-23 2016-03-16 小米科技有限责任公司 输入处理方法、装置及设备
US10613825B2 (en) * 2015-11-30 2020-04-07 Logmein, Inc. Providing electronic text recommendations to a user based on what is discussed during a meeting
US10049668B2 (en) 2015-12-02 2018-08-14 Apple Inc. Applying neural network language models to weighted finite state transducers for automatic speech recognition
CN106855796A (zh) 2015-12-09 2017-06-16 阿里巴巴集团控股有限公司 一种数据处理方法、装置和智能终端
US10714079B2 (en) * 2015-12-23 2020-07-14 Motorola Solutions, Inc. Methods and system for analyzing conversational statements and providing feedback in real-time
US10223066B2 (en) 2015-12-23 2019-03-05 Apple Inc. Proactive assistance based on dialog communication between devices
WO2017108143A1 (fr) * 2015-12-24 2017-06-29 Intel Corporation Entrée non linguistique pour la génération de langage naturel
US9978367B2 (en) 2016-03-16 2018-05-22 Google Llc Determining dialog states for language models
US11227589B2 (en) 2016-06-06 2022-01-18 Apple Inc. Intelligent list reading
US10049663B2 (en) 2016-06-08 2018-08-14 Apple, Inc. Intelligent automated assistant for media exploration
US10586535B2 (en) 2016-06-10 2020-03-10 Apple Inc. Intelligent digital assistant in a multi-tasking environment
DK179415B1 (en) 2016-06-11 2018-06-14 Apple Inc Intelligent device arbitration and control
US20170357411A1 (en) 2016-06-11 2017-12-14 Apple Inc. User interface for initiating a telephone call
DK201670540A1 (en) 2016-06-11 2018-01-08 Apple Inc Application integration with a digital assistant
DK179329B1 (en) 2016-06-12 2018-05-07 Apple Inc Handwriting keyboard for monitors
GB201610984D0 (en) 2016-06-23 2016-08-10 Microsoft Technology Licensing Llc Suppression of input images
US10832664B2 (en) 2016-08-19 2020-11-10 Google Llc Automated speech recognition using language models that selectively use domain-specific model components
US10474753B2 (en) 2016-09-07 2019-11-12 Apple Inc. Language identification using recurrent neural networks
US10043516B2 (en) 2016-09-23 2018-08-07 Apple Inc. Intelligent automated assistant
JP6758590B2 (ja) * 2016-09-23 2020-09-23 アップル インコーポレイテッドＡｐｐｌｅ Ｉｎｃ． ウォッチシアターモード
US20180101599A1 (en) * 2016-10-08 2018-04-12 Microsoft Technology Licensing, Llc Interactive context-based text completions
US9942312B1 (en) 2016-12-16 2018-04-10 Zoomdata, Inc. System and method for facilitating load reduction at a landing zone
US11204787B2 (en) 2017-01-09 2021-12-21 Apple Inc. Application integration with a digital assistant
US10311860B2 (en) 2017-02-14 2019-06-04 Google Llc Language model biasing system
US10318632B2 (en) 2017-03-14 2019-06-11 Microsoft Technology Licensing, Llc Multi-lingual data input system
US10417266B2 (en) 2017-05-09 2019-09-17 Apple Inc. Context-aware ranking of intelligent response suggestions
DK201770383A1 (en) 2017-05-09 2018-12-14 Apple Inc. USER INTERFACE FOR CORRECTING RECOGNITION ERRORS
DK180048B1 (en) 2017-05-11 2020-02-04 Apple Inc. MAINTAINING THE DATA PROTECTION OF PERSONAL INFORMATION
US10726832B2 (en) 2017-05-11 2020-07-28 Apple Inc. Maintaining privacy of personal information
US10395654B2 (en) 2017-05-11 2019-08-27 Apple Inc. Text normalization based on a data-driven learning network
DK179496B1 (en) 2017-05-12 2019-01-15 Apple Inc. USER-SPECIFIC Acoustic Models
DK179745B1 (en) 2017-05-12 2019-05-01 Apple Inc. SYNCHRONIZATION AND TASK DELEGATION OF A DIGITAL ASSISTANT
DK201770429A1 (en) 2017-05-12 2018-12-14 Apple Inc. LOW-LATENCY INTELLIGENT AUTOMATED ASSISTANT
US11301477B2 (en) 2017-05-12 2022-04-12 Apple Inc. Feedback analysis of a digital assistant
DK179549B1 (en) 2017-05-16 2019-02-12 Apple Inc. FAR-FIELD EXTENSION FOR DIGITAL ASSISTANT SERVICES
US20180336892A1 (en) 2017-05-16 2018-11-22 Apple Inc. Detecting a trigger of a digital assistant
US10303715B2 (en) 2017-05-16 2019-05-28 Apple Inc. Intelligent automated assistant for media exploration
US10311144B2 (en) 2017-05-16 2019-06-04 Apple Inc. Emoji word sense disambiguation
DK179555B1 (en) 2017-05-16 2019-02-13 Apple Inc. USER INTERFACE FOR A FLASHLIGHT MODE ON AN ELECTRONIC DEVICE
US11765114B2 (en) 2017-05-16 2023-09-19 Apple Inc. Voice communication method
US10403278B2 (en) 2017-05-16 2019-09-03 Apple Inc. Methods and systems for phonetic matching in digital assistant services
US10817568B2 (en) * 2017-06-05 2020-10-27 International Business Machines Corporation Domain-oriented predictive model feature recommendation system
CN107704946B (zh) * 2017-08-29 2019-05-10 平安科技（深圳）有限公司 电子装置、语音导航需求预测方法和存储介质
US10832668B1 (en) * 2017-09-19 2020-11-10 Amazon Technologies, Inc. Dynamic speech processing
CN107633052A (zh) * 2017-09-19 2018-01-26 王振江 机器人智能制造方法
CN107765979A (zh) * 2017-09-27 2018-03-06 北京金山安全软件有限公司 一种预测词的展示方法、装置及电子设备
US10733375B2 (en) 2018-01-31 2020-08-04 Apple Inc. Knowledge-based framework for improving natural language understanding
US10789959B2 (en) 2018-03-02 2020-09-29 Apple Inc. Training speaker recognition models for digital assistants
US10592604B2 (en) 2018-03-12 2020-03-17 Apple Inc. Inverse text normalization for automatic speech recognition
US10818288B2 (en) 2018-03-26 2020-10-27 Apple Inc. Natural assistant interaction
US10909331B2 (en) 2018-03-30 2021-02-02 Apple Inc. Implicit identification of translation payload with neural machine translation
US10474329B2 (en) 2018-04-09 2019-11-12 Capital One Services, Llc Selective generation and display of interfaces of a website or program
US10928918B2 (en) 2018-05-07 2021-02-23 Apple Inc. Raise to speak
US11145294B2 (en) 2018-05-07 2021-10-12 Apple Inc. Intelligent automated assistant for delivering content from user experiences
US10984780B2 (en) 2018-05-21 2021-04-20 Apple Inc. Global semantic word embeddings using bi-directional recurrent neural networks
US11386266B2 (en) 2018-06-01 2022-07-12 Apple Inc. Text correction
DK179822B1 (da) 2018-06-01 2019-07-12 Apple Inc. Voice interaction at a primary device to access call functionality of a companion device
US10892996B2 (en) 2018-06-01 2021-01-12 Apple Inc. Variable latency device coordination
DK201870355A1 (en) 2018-06-01 2019-12-16 Apple Inc. VIRTUAL ASSISTANT OPERATION IN MULTI-DEVICE ENVIRONMENTS
DK180639B1 (en) 2018-06-01 2021-11-04 Apple Inc DISABILITY OF ATTENTION-ATTENTIVE VIRTUAL ASSISTANT
US10496705B1 (en) 2018-06-03 2019-12-03 Apple Inc. Accelerated task performance
US11010561B2 (en) 2018-09-27 2021-05-18 Apple Inc. Sentiment prediction from textual data
US10839159B2 (en) 2018-09-28 2020-11-17 Apple Inc. Named entity normalization in a spoken dialog system
US11170166B2 (en) 2018-09-28 2021-11-09 Apple Inc. Neural typographical error modeling via generative adversarial networks
US11462215B2 (en) 2018-09-28 2022-10-04 Apple Inc. Multi-modal inputs for voice commands
US11475898B2 (en) 2018-10-26 2022-10-18 Apple Inc. Low-latency multi-speaker speech recognition
US11397770B2 (en) * 2018-11-26 2022-07-26 Sap Se Query discovery and interpretation
US11638059B2 (en) 2019-01-04 2023-04-25 Apple Inc. Content playback on multiple devices
US11348573B2 (en) 2019-03-18 2022-05-31 Apple Inc. Multimodality in digital assistant systems
DK201970509A1 (en) 2019-05-06 2021-01-15 Apple Inc Spoken notifications
US11475884B2 (en) 2019-05-06 2022-10-18 Apple Inc. Reducing digital assistant latency when a language is incorrectly determined
US11307752B2 (en) 2019-05-06 2022-04-19 Apple Inc. User configurable task triggers
US11423908B2 (en) 2019-05-06 2022-08-23 Apple Inc. Interpreting spoken requests
US11140099B2 (en) 2019-05-21 2021-10-05 Apple Inc. Providing message response suggestions
US11289073B2 (en) 2019-05-31 2022-03-29 Apple Inc. Device text to speech
DK180129B1 (en) 2019-05-31 2020-06-02 Apple Inc. USER ACTIVITY SHORTCUT SUGGESTIONS
US11496600B2 (en) 2019-05-31 2022-11-08 Apple Inc. Remote execution of machine-learned models
DK201970511A1 (en) 2019-05-31 2021-02-15 Apple Inc Voice identification in digital assistant systems
US11360641B2 (en) 2019-06-01 2022-06-14 Apple Inc. Increasing the relevance of new available information
US11468890B2 (en) 2019-06-01 2022-10-11 Apple Inc. Methods and user interfaces for voice-based control of electronic devices
US10878782B1 (en) 2019-09-09 2020-12-29 Apple Inc. Techniques for managing display usage
US11488406B2 (en) 2019-09-25 2022-11-01 Apple Inc. Text detection using global geometry estimators
US11189271B2 (en) 2020-02-17 2021-11-30 Cerence Operating Company Coordinating electronic personal assistants
US11061543B1 (en) 2020-05-11 2021-07-13 Apple Inc. Providing relevant data items based on context
US11183193B1 (en) 2020-05-11 2021-11-23 Apple Inc. Digital assistant hardware abstraction
US11490204B2 (en) 2020-07-20 2022-11-01 Apple Inc. Multi-device audio adjustment coordination
US11438683B2 (en) 2020-07-21 2022-09-06 Apple Inc. User identification using headphones
US11893203B2 (en) * 2021-08-31 2024-02-06 Apple Inc. Methods and interfaces for initiating communications

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1256875A1 (fr) * 2001-05-10 2002-11-13 Nokia Corporation Procédé et dispositif pour la prédiction des saisies de l'utilisateur selon le contexte
WO2004110040A1 (fr) * 2003-06-10 2004-12-16 Symbian Software Limited Procede permettant a un dispositif d'information sans fil de modifier automatiquement son comportement
EP1522920A2 (fr) * 2003-09-05 2005-04-13 Samsung Electronics Co., Ltd. Interface utilisateur proactive avec un agent émotionnel

Family Cites Families (297)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3967273A (en) 1974-03-29 1976-06-29 Bell Telephone Laboratories, Incorporated Method and apparatus for using pushbutton telephone keys for generation of alpha-numeric information
US4003025A (en) 1975-12-24 1977-01-11 International Business Machines Corporation Alphabetic character word upper/lower case print convention apparatus and method
US4164025A (en) 1977-12-13 1979-08-07 Bell Telephone Laboratories, Incorporated Spelled word input directory information retrieval system with input word error corrective searching
US4191854A (en) 1978-01-06 1980-03-04 Coles George A Telephone-coupled visual alphanumeric communication device for deaf persons
JPS6239467Y2 (fr) 1978-11-20 1987-10-08
US4360892A (en) 1979-02-22 1982-11-23 Microwriter Limited Portable word-processor
US4679951A (en) 1979-11-06 1987-07-14 Cornell Research Foundation, Inc. Electronic keyboard system and method for reproducing selected symbolic language characters
US4464070A (en) 1979-12-26 1984-08-07 International Business Machines Corporation Multi-character display controller for text recorder
JPS56143044A (en) 1980-04-08 1981-11-07 Sony Corp Input device of english typewriter
WO1982000442A1 (fr) 1980-08-01 1982-02-18 R Johnson Systeme de selection de mots ideographiques
US4442506A (en) 1980-09-18 1984-04-10 Microwriter Limited Portable word-processor
JPS57109031A (en) 1980-12-26 1982-07-07 Sharp Corp Input equipment
US4427848B1 (en) 1981-12-29 1994-03-29 Telephone Lottery Company Inc Telephonic alphanumeric data transmission system
JPS58175074A (ja) 1982-04-07 1983-10-14 Toshiba Corp 構文分析方式
US5067103A (en) 1983-01-21 1991-11-19 The Laitram Corporation Hand held computers with alpha keystroke
US4891786A (en) 1983-02-22 1990-01-02 Goldwasser Eric P Stroke typing system
USRE32773E (en) 1983-02-22 1988-10-25 Method of creating text using a computer
US4544276A (en) 1983-03-21 1985-10-01 Cornell Research Foundation, Inc. Method and apparatus for typing Japanese text using multiple systems
US5289394A (en) 1983-05-11 1994-02-22 The Laitram Corporation Pocket computer for word processing
US4649563A (en) 1984-04-02 1987-03-10 R L Associates Method of and means for accessing computerized data bases utilizing a touch-tone telephone instrument
JPS619753A (ja) 1984-06-26 1986-01-17 Hitachi Ltd 文書処理装置における頻発熟語の自動登録方法
US4791556A (en) 1984-08-29 1988-12-13 Vilkaitis John V Method for operating a computer which searches for operational symbols and executes functions corresponding to the operational symbols in response to user inputted signal
US4661916A (en) 1984-10-15 1987-04-28 Baker Bruce R System for method for producing synthetic plural word messages
US4650927A (en) 1984-11-29 1987-03-17 International Business Machines Corporation Processor-assisted communication system using tone-generating telephones
JPS61282965A (ja) 1985-06-07 1986-12-13 Nec Corp 文字列分割方法
US4677659A (en) 1985-09-03 1987-06-30 John Dargan Telephonic data access and transmission system
US4669901A (en) 1985-09-03 1987-06-02 Feng I Ming Keyboard device for inputting oriental characters by touch
US4674112A (en) 1985-09-06 1987-06-16 Board Of Regents, The University Of Texas System Character pattern recognition and communications apparatus
US4969097A (en) 1985-09-18 1990-11-06 Levin Leonid D Method of rapid entering of text into computer equipment
US4754474A (en) 1985-10-21 1988-06-28 Feinson Roy W Interpretive tone telecommunication method and apparatus
US4807181A (en) 1986-06-02 1989-02-21 Smith Corona Corporation Dictionary memory with visual scanning from a selectable starting point
US4817129A (en) 1987-03-05 1989-03-28 Telac Corp. Method of and means for accessing computerized data bases utilizing a touch-tone telephone instrument
US5041967A (en) * 1987-10-13 1991-08-20 Bell Communications Research, Inc. Methods and apparatus for dynamic menu generation in a menu driven computer system
US4852173A (en) 1987-10-29 1989-07-25 International Business Machines Corporation Design and construction of a binary-tree system for language modelling
US5031206A (en) 1987-11-30 1991-07-09 Fon-Ex, Inc. Method and apparatus for identifying words entered on DTMF pushbuttons
US4866759A (en) 1987-11-30 1989-09-12 Riskin Bernard N Packet network telecommunication system having access nodes with word guessing capability
JPH01167898A (ja) 1987-12-04 1989-07-03 Internatl Business Mach Corp <Ibm> 音声認識装置
US4872196A (en) 1988-07-18 1989-10-03 Motorola, Inc. Telephone keypad input technique
US5109352A (en) 1988-08-09 1992-04-28 Dell Robert B O System for encoding a collection of ideographic characters
JPH02117218A (ja) 1988-10-27 1990-05-01 Toshiba Corp 命令指示装置
US5115501A (en) * 1988-11-04 1992-05-19 International Business Machines Corporation Procedure for automatically customizing the user interface of application programs
JPH02150899A (ja) 1988-12-02 1990-06-11 Toshiba Corp 音声認識方式
CA2006163A1 (fr) 1988-12-21 1990-06-21 Alfred B. Freeman Systeme de frappe express
US5371851A (en) 1989-04-26 1994-12-06 Credence Systems Corporation Graphical data base editor
US5163084A (en) 1989-08-11 1992-11-10 Korea Telecommunication Authority Voice information service system and method utilizing approximately matched input character string and key word
US5255310A (en) 1989-08-11 1993-10-19 Korea Telecommunication Authority Method of approximately matching an input character string with a key word and vocally outputting data
CA1336418C (fr) 1989-08-29 1995-07-25 Edgar Matias Clavier pour une seule main
JPH03141460A (ja) 1989-10-27 1991-06-17 Hitachi Ltd 仮名漢字変換方式
US5267181A (en) 1989-11-03 1993-11-30 Handykey Corporation Cybernetic interface for a computer that uses a hand held chord keyboard
US5339358A (en) 1990-03-28 1994-08-16 Danish International, Inc. Telephone keypad matrix
US5392338A (en) 1990-03-28 1995-02-21 Danish International, Inc. Entry of alphabetical characters into a telephone system using a conventional telephone keypad
US5131045A (en) 1990-05-10 1992-07-14 Roth Richard G Audio-augmented data keying
US5303299A (en) 1990-05-15 1994-04-12 Vcs Industries, Inc. Method for continuous recognition of alphanumeric strings spoken over a telephone network
CA2045906A1 (fr) 1990-06-29 1991-12-30 Wei Zhang Appareil a rendement eleve pour le traitement de signaux d'ecriture alphabetique
US5305205A (en) 1990-10-23 1994-04-19 Weber Maria L Computer-assisted transcription apparatus
US5128672A (en) 1990-10-30 1992-07-07 Apple Computer, Inc. Dynamic predictive keyboard
US5210689A (en) 1990-12-28 1993-05-11 Semantic Compaction Systems System and method for automatically selecting among a plurality of input modes
US5229936A (en) 1991-01-04 1993-07-20 Franklin Electronic Publishers, Incorporated Device and method for the storage and retrieval of inflection information for electronic reference products
US5714983A (en) * 1991-02-20 1998-02-03 Sacks; Robert Interactive computer system with controllable rate repetition of computer actions in response to operator inputs
US5200988A (en) 1991-03-11 1993-04-06 Fon-Ex, Inc. Method and means for telecommunications by deaf persons utilizing a small hand held communications device
US5258748A (en) 1991-08-28 1993-11-02 Hewlett-Packard Company Accessing and selecting multiple key functions with minimum keystrokes
US5528235A (en) 1991-09-03 1996-06-18 Edward D. Lin Multi-status multi-function data processing key and key array
US5373290A (en) 1991-09-25 1994-12-13 Hewlett-Packard Corporation Apparatus and method for managing multiple dictionaries in content addressable memory based data compression
US5963671A (en) 1991-11-27 1999-10-05 International Business Machines Corporation Enhancement of soft keyboard operations using trigram prediction
US5903454A (en) * 1991-12-23 1999-05-11 Hoffberg; Linda Irene Human-factored interface corporating adaptive pattern recognition based controller apparatus
US6081750A (en) * 1991-12-23 2000-06-27 Hoffberg; Steven Mark Ergonomic man-machine interface incorporating adaptive pattern recognition based control system
JP2640793B2 (ja) 1992-01-17 1997-08-13 松下電器産業株式会社 共起辞書構築装置及びこの共起辞書を用いた文解析装置
JPH05265682A (ja) 1992-03-19 1993-10-15 Fujitsu Ltd メニュー階層変更方式
US5535421A (en) 1993-03-16 1996-07-09 Weinreich; Michael Chord keyboard system using one chord to select a group from among several groups and another chord to select a character from the selected group
JPH0756957A (ja) 1993-08-03 1995-03-03 Xerox Corp ユーザへの情報提供方法
US5586198A (en) 1993-08-24 1996-12-17 Lakritz; David Method and apparatus for identifying characters in ideographic alphabet
US5388061A (en) 1993-09-08 1995-02-07 Hankes; Elmer J. Portable computer for one-handed operation
GB2283598A (en) 1993-11-03 1995-05-10 Ibm Data entry workstation
CA2137981C (fr) 1993-12-22 2001-04-17 Steven A. Shaiman Methode et systeme utilisant l'apprentissage adaptatif pour presenter un choix d'options
JP3523343B2 (ja) 1993-12-27 2004-04-26 ミサワホーム株式会社 キー入力装置およびキー入力方法
US6008799A (en) 1994-05-24 1999-12-28 Microsoft Corporation Method and system for entering data using an improved on-screen keyboard
JPH086939A (ja) 1994-06-20 1996-01-12 Nippon Telegr & Teleph Corp <Ntt> 通信文作成装置及び通信文作成方法
US5774860A (en) * 1994-06-27 1998-06-30 U S West Technologies, Inc. Adaptive knowledge base of complex information through interactive voice dialogue
US5847697A (en) 1995-01-31 1998-12-08 Fujitsu Limited Single-handed keyboard having keys with multiple characters and character ambiguity resolution logic
US5805911A (en) 1995-02-01 1998-09-08 Microsoft Corporation Word prediction system
US5748512A (en) 1995-02-28 1998-05-05 Microsoft Corporation Adjusting keyboard
DE69613105T2 (de) 1995-03-08 2002-01-31 Ericsson Telefon Ab L M Verfahren zur auswahl von zeichen aus einer tastatur und telefonapparat, der mit diesem verfahren arbeitet
US5786776A (en) 1995-03-13 1998-07-28 Kabushiki Kaisha Toshiba Character input terminal device and recording apparatus
US5559512A (en) 1995-03-20 1996-09-24 Venturedyne, Ltd. Method and apparatus for entering alpha-numeric data
US5623261A (en) 1995-04-17 1997-04-22 International Business Machines Corporation Method and system for translating keyed input within a data processing system
US6734881B1 (en) 1995-04-18 2004-05-11 Craig Alexander Will Efficient entry of words by disambiguation
US5825353A (en) 1995-04-18 1998-10-20 Will; Craig Alexander Control of miniature personal digital assistant using menu and thumbwheel
US6392640B1 (en) 1995-04-18 2002-05-21 Cognitive Research & Design Corp. Entry of words with thumbwheel by disambiguation
SE516771C2 (sv) 1995-05-30 2002-02-26 Minec Systems Ab Alfanumeriskt tangentbord
US5680511A (en) 1995-06-07 1997-10-21 Dragon Systems, Inc. Systems and methods for word recognition
US5918240A (en) 1995-06-28 1999-06-29 Xerox Corporation Automatic method of extracting summarization using feature probabilities
US5828991A (en) 1995-06-30 1998-10-27 The Research Foundation Of The State University Of New York Sentence reconstruction using word ambiguity resolution
US5797098A (en) 1995-07-19 1998-08-18 Pacific Communication Sciences, Inc. User interface for cellular telephone
US5818437A (en) 1995-07-26 1998-10-06 Tegic Communications, Inc. Reduced keyboard disambiguating computer
CA2227904C (fr) 1995-07-26 2000-11-14 Tegic Communications, Inc. Systeme d'elimination des ambiguites d'un clavier reduit
US5917941A (en) 1995-08-08 1999-06-29 Apple Computer, Inc. Character segmentation technique with integrated word search for handwriting recognition
US5855000A (en) 1995-09-08 1998-12-29 Carnegie Mellon University Method and apparatus for correcting and repairing machine-transcribed input using independent or cross-modal secondary input
EP0795170B1 (fr) 1995-09-29 2003-01-15 AT&T Corp. Service du reseau telephonique servant a convertir la parole en tonalites d'appel
US5726688A (en) * 1995-09-29 1998-03-10 Ncr Corporation Predictive, adaptive computer interface
JPH09114817A (ja) 1995-10-13 1997-05-02 Matsushita Electric Ind Co Ltd 文字入力装置
JPH09134243A (ja) 1995-11-10 1997-05-20 Hitachi Ltd バックライト付携帯端末装置
US6064959A (en) 1997-03-28 2000-05-16 Dragon Systems, Inc. Error correction in speech recognition
US5794189A (en) 1995-11-13 1998-08-11 Dragon Systems, Inc. Continuous speech recognition
US5917890A (en) 1995-12-29 1999-06-29 At&T Corp Disambiguation of alphabetic characters in an automated call processing environment
US6073101A (en) 1996-02-02 2000-06-06 International Business Machines Corporation Text independent speaker recognition for transparent command ambiguity resolution and continuous access control
JP3724036B2 (ja) 1996-02-06 2005-12-07 富士通株式会社 情報入力装置
JP3113814B2 (ja) 1996-04-17 2000-12-04 インターナショナル・ビジネス・マシーンズ・コーポレ−ション 情報検索方法及び情報検索装置
TW421764B (en) 1996-05-21 2001-02-11 Hitachi Ltd Input character string estimation and identification apparatus
US6453281B1 (en) 1996-07-30 2002-09-17 Vxi Corporation Portable audio database device with icon-based graphical user-interface
US5864340A (en) * 1996-08-22 1999-01-26 International Business Machines Corporation Mobile client computer programmed to predict input
US5664896A (en) 1996-08-29 1997-09-09 Blumberg; Marvin R. Speed typing apparatus and method
US5950160A (en) 1996-10-31 1999-09-07 Microsoft Corporation Method and system for displaying a variable number of alternative words during speech recognition
US5926566A (en) 1996-11-15 1999-07-20 Synaptics, Inc. Incremental ideographic character input method
US5952942A (en) 1996-11-21 1999-09-14 Motorola, Inc. Method and device for input of text messages from a keypad
US5953541A (en) 1997-01-24 1999-09-14 Tegic Communications, Inc. Disambiguating system for disambiguating ambiguous input sequences by displaying objects associated with the generated input sequences in the order of decreasing frequency of use
US6009444A (en) 1997-02-24 1999-12-28 Motorola, Inc. Text input device and method
US6005495A (en) 1997-02-27 1999-12-21 Ameritech Corporation Method and system for intelligent text entry on a numeric keypad
CN1227914C (zh) 1997-03-19 2005-11-16 西门子公司 具有用于文字和/或数字和/或特殊字符输入工具的设备
US5937422A (en) 1997-04-15 1999-08-10 The United States Of America As Represented By The National Security Agency Automatically generating a topic description for text and searching and sorting text by topic using the same
US6054941A (en) 1997-05-27 2000-04-25 Motorola, Inc. Apparatus and method for inputting ideographic characters
WO1999000790A1 (fr) 1997-06-27 1999-01-07 M.H. Segan Limited Partnership Dispositif et entree d'ordinateur reconnaissant la parole
US5936556A (en) 1997-07-14 1999-08-10 Sakita; Masami Keyboard for inputting to computer means
US6246761B1 (en) 1997-07-24 2001-06-12 Nortel Networks Limited Automatic volume control for a telephone ringer
US6044347A (en) 1997-08-05 2000-03-28 Lucent Technologies Inc. Methods and apparatus object-oriented rule-based dialogue management
US5999950A (en) 1997-08-11 1999-12-07 Webtv Networks, Inc. Japanese text input method using a keyboard with only base kana characters
US6098086A (en) 1997-08-11 2000-08-01 Webtv Networks, Inc. Japanese text input method using a limited roman character set
US6120297A (en) 1997-08-25 2000-09-19 Lyceum Communication, Inc. Vocabulary acquistion using structured inductive reasoning
DE19839793B4 (de) 1997-09-02 2008-08-07 Denso Corp., Kariya Datenwiedergewinnungs- und -anzeigesystem
EP1018069B1 (fr) 1997-09-25 2002-07-24 Tegic Communications, Inc. Systeme de resolution d'ambiguites pour clavier reduit
CA2219008C (fr) 1997-10-21 2002-11-19 Bell Canada Methode et appareil permettant d'ameliorer l'utilite de la reconnaissance de la parole
US6005498A (en) 1997-10-29 1999-12-21 Motorola, Inc. Reduced keypad entry apparatus and method
US6377965B1 (en) 1997-11-07 2002-04-23 Microsoft Corporation Automatic word completion system for partially entered data
US5896321A (en) 1997-11-14 1999-04-20 Microsoft Corporation Text completion system for a miniature computer
TW476033B (en) 1997-11-19 2002-02-11 Inventec Corp System and method for increasing word recognition rate
US5945928A (en) 1998-01-20 1999-08-31 Tegic Communication, Inc. Reduced keyboard disambiguating system for the Korean language
JP3532780B2 (ja) 1998-01-22 2004-05-31 テジック・コミュニケーションズ・インコーポレーテッド 音声仮名文字の入力順序を生成するための入力システム
US7257528B1 (en) 1998-02-13 2007-08-14 Zi Corporation Of Canada, Inc. Method and apparatus for Chinese character text input
US6104317A (en) 1998-02-27 2000-08-15 Motorola, Inc. Data entry device and method
US6574597B1 (en) 1998-05-08 2003-06-03 At&T Corp. Fully expanded context-dependent networks for speech recognition
US6121968A (en) 1998-06-17 2000-09-19 Microsoft Corporation Adaptive menus
JP3191284B2 (ja) 1998-06-23 2001-07-23 日本電気株式会社 文字入力装置
US6407679B1 (en) 1998-07-31 2002-06-18 The Research Foundation Of The State University Of New York System and method for entering text in a virtual environment
US6169538B1 (en) 1998-08-13 2001-01-02 Motorola, Inc. Method and apparatus for implementing a graphical user interface keyboard and a text buffer on electronic devices
SE514377C2 (sv) 1998-08-26 2001-02-19 Gunnar Sparr Teckenigenkänning
US6178401B1 (en) 1998-08-28 2001-01-23 International Business Machines Corporation Method for reducing search complexity in a speech recognition system
US6684185B1 (en) 1998-09-04 2004-01-27 Matsushita Electric Industrial Co., Ltd. Small footprint language and vocabulary independent word recognizer using registration by word spelling
US6636162B1 (en) 1998-12-04 2003-10-21 America Online, Incorporated Reduced keyboard text input system for the Japanese language
US7679534B2 (en) 1998-12-04 2010-03-16 Tegic Communications, Inc. Contextual prediction of user words and user actions
US7712053B2 (en) 1998-12-04 2010-05-04 Tegic Communications, Inc. Explicit character filtering of ambiguous text entry
US7881936B2 (en) 1998-12-04 2011-02-01 Tegic Communications, Inc. Multimodal disambiguation of speech recognition
US6646573B1 (en) 1998-12-04 2003-11-11 America Online, Inc. Reduced keyboard text input system for the Japanese language
US6219731B1 (en) 1998-12-10 2001-04-17 Eaton: Ergonomics, Inc. Method and apparatus for improved multi-tap text input
CA2353862C (fr) 1998-12-10 2007-11-13 Eatoni Ergonomics, Inc. Dispositifs a frappe a simple toucher dont le fonctionnement est fonde sur des codes ambigus, et procedes de conception de tels dispositifs
US6885317B1 (en) 1998-12-10 2005-04-26 Eatoni Ergonomics, Inc. Touch-typable devices based on ambiguous codes and methods to design such devices
US6466232B1 (en) * 1998-12-18 2002-10-15 Tangis Corporation Method and system for controlling presentation of information to a user based on the user's condition
US6362752B1 (en) 1998-12-23 2002-03-26 Motorola, Inc. Keypad with strokes assigned to key for ideographic text input
US6801659B1 (en) 1999-01-04 2004-10-05 Zi Technology Corporation Ltd. Text input system for ideographic and nonideographic languages
US6393395B1 (en) 1999-01-07 2002-05-21 Microsoft Corporation Handwriting and speech recognizer using neural network with separate start and continuation output scores
US6278968B1 (en) 1999-01-29 2001-08-21 Sony Corporation Method and apparatus for adaptive speech recognition hypothesis construction and selection in a spoken language translation system
GB2388938B (en) 1999-02-22 2004-03-17 Nokia Corp A communication terminal having a predictive editor application
GB2347247A (en) 1999-02-22 2000-08-30 Nokia Mobile Phones Ltd Communication terminal with predictive editor
GB2347240A (en) * 1999-02-22 2000-08-30 Nokia Mobile Phones Ltd Communication terminal having a predictive editor application
US6567075B1 (en) 1999-03-19 2003-05-20 Avaya Technology Corp. Feature access control in a display-based terminal environment
US6204848B1 (en) 1999-04-14 2001-03-20 Motorola, Inc. Data entry apparatus having a limited number of character keys and method
US7030863B2 (en) 2000-05-26 2006-04-18 America Online, Incorporated Virtual keyboard system with automatic correction
AU5299700A (en) 1999-05-27 2000-12-18 America Online, Inc. Keyboard system with automatic correction
US6172625B1 (en) 1999-07-06 2001-01-09 Motorola, Inc. Disambiguation method and apparatus, and dictionary data compression techniques
US20010003184A1 (en) 1999-07-16 2001-06-07 Ching Jamison K. Methods and articles of manufacture for interfacing, advertising and navigating with internet television
US6421672B1 (en) 1999-07-27 2002-07-16 Verizon Services Corp. Apparatus for and method of disambiguation of directory listing searches utilizing multiple selectable secondary search keys
US6789231B1 (en) 1999-10-05 2004-09-07 Microsoft Corporation Method and system for providing alternatives for text derived from stochastic input sources
US6424743B1 (en) 1999-11-05 2002-07-23 Motorola, Inc. Graphical handwriting recognition user interface
US6848080B1 (en) 1999-11-05 2005-01-25 Microsoft Corporation Language input architecture for converting one text form to another text form with tolerance to spelling, typographical, and conversion errors
US6633846B1 (en) 1999-11-12 2003-10-14 Phoenix Solutions, Inc. Distributed realtime speech recognition system
US6665640B1 (en) 1999-11-12 2003-12-16 Phoenix Solutions, Inc. Interactive speech based learning/training system formulating search queries based on natural language parsing of recognized user queries
US9076448B2 (en) 1999-11-12 2015-07-07 Nuance Communications, Inc. Distributed real time speech recognition system
JP2001224075A (ja) 2000-02-10 2001-08-17 Nec Shizuoka Ltd 携帯型移動通信端末のマナーモード設定システム及び方法
US6621424B1 (en) 2000-02-18 2003-09-16 Mitsubishi Electric Research Laboratories Inc. Method for predicting keystroke characters on single pointer keyboards and apparatus therefore
JP3508840B2 (ja) 2000-03-07 2004-03-22 日本電気株式会社 携帯電話機
US20020054135A1 (en) 2000-03-17 2002-05-09 Masahiro Noguchi Information processing device, information processsing method, and computer-readable storage medium storing program for executing this method on a computer
US6304844B1 (en) 2000-03-30 2001-10-16 Verbaltek, Inc. Spelling speech recognition apparatus and method for communications
US7076255B2 (en) * 2000-04-05 2006-07-11 Microsoft Corporation Context-aware and location-aware cellular phones and methods
US7107204B1 (en) 2000-04-24 2006-09-12 Microsoft Corporation Computer-aided writing system and method with cross-language writing wizard
JP2001325252A (ja) 2000-05-12 2001-11-22 Sony Corp 携帯端末及びその情報入力方法、辞書検索装置及び方法、媒体
US6985933B1 (en) 2000-05-30 2006-01-10 International Business Machines Corporation Method and system for increasing ease-of-use and bandwidth utilization in wireless devices
JP4346217B2 (ja) 2000-06-28 2009-10-21 株式会社東芝 携帯端末
JP2002014856A (ja) 2000-06-29 2002-01-18 Makoto Oshima データファイル検索・管理システム
JP4105841B2 (ja) 2000-07-11 2008-06-25 インターナショナル・ビジネス・マシーンズ・コーポレーション 音声認識方法、音声認識装置、コンピュータ・システムおよび記憶媒体
US6856651B2 (en) 2000-07-25 2005-02-15 Peribit Networks, Inc. System and method for incremental and continuous data compression
TW472232B (en) 2000-08-11 2002-01-11 Ind Tech Res Inst Probability-base fault-tolerance natural language understanding method
US6686852B1 (en) 2000-09-15 2004-02-03 Motorola, Inc. Keypad layout for alphabetic character input
JP2002141996A (ja) 2000-11-02 2002-05-17 Omron Corp 携帯電話機
US6728348B2 (en) 2000-11-30 2004-04-27 Comverse, Inc. System for storing voice recognizable identifiers using a limited input device such as a telephone key pad
US20020072395A1 (en) 2000-12-08 2002-06-13 Ivan Miramontes Telephone with fold out keyboard
SE521911C2 (sv) 2001-01-15 2003-12-16 Decuma Ab Ideon Res Park Metod, anordning och datorprogram för igenkänning av ett handskrivet tecken
US7439957B2 (en) 2001-01-25 2008-10-21 International Business Machines Corporation Compact universal keyboard
US7027976B1 (en) 2001-01-29 2006-04-11 Adobe Systems Incorporated Document based character ambiguity resolution
US6868280B2 (en) 2001-03-02 2005-03-15 Nokia Corporation Method and apparatus for entering information in a mobile device with special characters
US7013258B1 (en) 2001-03-07 2006-03-14 Lenovo (Singapore) Pte. Ltd. System and method for accelerating Chinese text input
US20020126097A1 (en) 2001-03-07 2002-09-12 Savolainen Sampo Jussi Pellervo Alphanumeric data entry method and apparatus using reduced keyboard and context related dictionaries
SE519014C2 (sv) 2001-03-07 2002-12-23 Decuma Ab Ideon Res Park Metod och anordning för igenkänning av ett handskrivet mönster
US6502118B1 (en) 2001-03-22 2002-12-31 Motorola, Inc. Fast system and method for producing a logarithmic signal approximation with variable precision
US6982658B2 (en) 2001-03-22 2006-01-03 Motorola, Inc. Keypad layout for alphabetic symbol input
US7385591B2 (en) 2001-03-31 2008-06-10 Microsoft Corporation Out-of-vocabulary word determination and user interface for text input via reduced keypad keys
US7103534B2 (en) 2001-03-31 2006-09-05 Microsoft Corporation Machine learning contextual approach to word determination for text input via reduced keypad keys
US7117144B2 (en) 2001-03-31 2006-10-03 Microsoft Corporation Spell checking for text input via reduced keypad keys
US20020152075A1 (en) 2001-04-16 2002-10-17 Shao-Tsu Kung Composite input method
JP4084582B2 (ja) 2001-04-27 2008-04-30 俊司 加藤 タッチ式キー入力装置
GB0111012D0 (en) 2001-05-04 2001-06-27 Nokia Corp A communication terminal having a predictive text editor application
JP2002351862A (ja) 2001-05-28 2002-12-06 Sony Corp 携帯情報端末、通信端末、およびコンピュータプログラム
US20030023426A1 (en) 2001-06-22 2003-01-30 Zi Technology Corporation Ltd. Japanese language entry mechanism for small keypads
KR101131003B1 (ko) 2001-07-12 2012-03-28 벤자민 피루쯔 가사빈 텍스트 입력 시스템으로 데이터를 입력하는 방법
US20030036411A1 (en) 2001-08-03 2003-02-20 Christian Kraft Method of entering characters into a text string and a text-editing terminal using the method
US6947771B2 (en) 2001-08-06 2005-09-20 Motorola, Inc. User interface for a portable electronic device
US6757544B2 (en) 2001-08-15 2004-06-29 Motorola, Inc. System and method for determining a location relevant to a communication device and/or its associated user
US20030054830A1 (en) 2001-09-04 2003-03-20 Zi Corporation Navigation system for mobile communication devices
WO2004023455A2 (fr) 2002-09-06 2004-03-18 Voice Signal Technologies, Inc. Procedes, systemes et programmation destines a la realisation de reconnaissance vocale
JP2003108472A (ja) 2001-09-28 2003-04-11 Canon Inc 通信装置、その制御方法、情報機器、その制御方法、通信システム、及び制御プログラム
JP2003116179A (ja) 2001-10-02 2003-04-18 Yoshiaki Masuno 携帯端末情報交信システム
US7152213B2 (en) 2001-10-04 2006-12-19 Infogation Corporation System and method for dynamic key assignment in enhanced user interface
US7006820B1 (en) 2001-10-05 2006-02-28 At Road, Inc. Method for determining preferred conditions for wireless programming of mobile devices
US20030088398A1 (en) 2001-11-08 2003-05-08 Jin Guo User interface of a keypad entry system for korean text input
US6912386B1 (en) 2001-11-13 2005-06-28 Nokia Corporation Method for controlling operation of a mobile device by detecting usage situations
US20030090515A1 (en) * 2001-11-13 2003-05-15 Sony Corporation And Sony Electronics Inc. Simplified user interface by adaptation based on usage history
US6765556B2 (en) 2001-11-16 2004-07-20 International Business Machines Corporation Two-key input per character text entry apparatus and method
US6744423B2 (en) 2001-11-19 2004-06-01 Nokia Corporation Communication terminal having a predictive character editor application
US7149550B2 (en) 2001-11-27 2006-12-12 Nokia Corporation Communication terminal having a text editor application with a word completion feature
US20030101060A1 (en) 2001-11-29 2003-05-29 Bickley Corine A. Use of historical data for a voice application interface
US7075520B2 (en) 2001-12-12 2006-07-11 Zi Technology Corporation Ltd Key press disambiguation using a keypad of multidirectional keys
GB2383459B (en) 2001-12-20 2005-05-18 Hewlett Packard Co Speech recognition system and method
US6934564B2 (en) 2001-12-20 2005-08-23 Nokia Corporation Method and apparatus for providing Hindi input to a device using a numeric keypad
US20030119561A1 (en) 2001-12-21 2003-06-26 Richard Hatch Electronic device
JP2003196273A (ja) 2001-12-27 2003-07-11 Mitsubishi Electric Corp 文字列入力装置および文字列入力方法
DE10201195B4 (de) 2002-01-14 2006-10-12 Völckers, Oliver Verfahren zur Texteingabe durch Auswahl von Buchstaben mittels eines Cursors und Einrichtung zur Durchführung des Verfahrens
US7111248B2 (en) 2002-01-15 2006-09-19 Openwave Systems Inc. Alphanumeric information input method
US7949513B2 (en) 2002-01-22 2011-05-24 Zi Corporation Of Canada, Inc. Language module and method for use with text processing devices
US6807529B2 (en) 2002-02-27 2004-10-19 Motorola, Inc. System and method for concurrent multimodal communication
US6912581B2 (en) 2002-02-27 2005-06-28 Motorola, Inc. System and method for concurrent multimodal communication session persistence
US6864809B2 (en) 2002-02-28 2005-03-08 Zi Technology Corporation Ltd Korean language predictive mechanism for text entry by a user
EP1347361A1 (fr) 2002-03-22 2003-09-24 Sony Ericsson Mobile Communications AB Saisie de text dans un dispositif électronique de communication
US7272564B2 (en) 2002-03-22 2007-09-18 Motorola, Inc. Method and apparatus for multimodal communication with user control of delivery modality
ATE295567T1 (de) 2002-03-22 2005-05-15 Sony Ericsson Mobile Comm Ab Texteingabe in ein elektronisches kommunikationsgerät
SG125895A1 (en) 2002-04-04 2006-10-30 Xrgomics Pte Ltd Reduced keyboard system that emulates qwerty-type mapping and typing
US7174288B2 (en) 2002-05-08 2007-02-06 Microsoft Corporation Multi-modal entry of ideogrammatic languages
US20040198427A1 (en) 2002-05-21 2004-10-07 Kimbell Benjamin D. System and method for incoming communication management for a cummunication device
US7020849B1 (en) 2002-05-31 2006-03-28 Openwave Systems Inc. Dynamic display for communication devices
TW559783B (en) 2002-05-31 2003-11-01 Ind Tech Res Inst Error-tolerant natural language understanding system and method integrating with confidence measure
RU2316040C2 (ru) 2002-07-01 2008-01-27 Сони Эрикссон Мобайл Коммьюникейшнз Аб Ввод текста в электронное устройство связи
CA2410057C (fr) 2002-07-03 2008-04-29 2012244 Ontario Inc. Appareil et methode d'entree de syllabes coreennes ideographiques a partir d'un clavier reduit
US7386454B2 (en) 2002-07-31 2008-06-10 International Business Machines Corporation Natural error handling in speech recognition
US20040052355A1 (en) 2002-09-12 2004-03-18 International Business Machines Corporation Apparatus, system and method of auto-completing telephone numbers that are being manually dialed
FI20021759A0 (fi) 2002-10-03 2002-10-03 Nokia Corp Menetelmä ja käyttöliittymä tekstin syöttämiseen
US6990333B2 (en) * 2002-11-27 2006-01-24 Microsoft Corporation System and method for timed profile changes on a mobile device
US7199786B2 (en) 2002-11-29 2007-04-03 Daniel Suraqui Reduced keyboards system using unistroke input and having automatic disambiguating and a recognition method using said system
US7095403B2 (en) 2002-12-09 2006-08-22 Motorola, Inc. User interface of a keypad entry system for character input
US20040163032A1 (en) 2002-12-17 2004-08-19 Jin Guo Ambiguity resolution for predictive text entry
US6993482B2 (en) 2002-12-18 2006-01-31 Motorola, Inc. Method and apparatus for displaying speech recognition results
US6927763B2 (en) 2002-12-30 2005-08-09 Motorola, Inc. Method and system for providing a disambiguated keypad
US20040127198A1 (en) * 2002-12-30 2004-07-01 Roskind James A. Automatically changing a mobile device configuration based on environmental condition
US20040127197A1 (en) 2002-12-30 2004-07-01 Roskind James A. Automatically changing a mobile device configuration
US7098896B2 (en) 2003-01-16 2006-08-29 Forword Input Inc. System and method for continuous stroke word-based text input
EP1586191A4 (fr) 2003-01-22 2009-03-11 Min-Kyum Kim Appareil et procede pour la saisie de caracteres alphabetiques
US20040153975A1 (en) 2003-02-05 2004-08-05 Williams Roland E. Text entry mechanism for small keypads
US20040153963A1 (en) 2003-02-05 2004-08-05 Simpson Todd G. Information entry mechanism for small keypads
US7256769B2 (en) 2003-02-24 2007-08-14 Zi Corporation Of Canada, Inc. System and method for text entry on a reduced keyboard
US7386279B2 (en) 2003-04-02 2008-06-10 Sun Microsystems, Inc. Context based main screen for mobile device
US6955602B2 (en) 2003-05-15 2005-10-18 Zi Technology Corporation Ltd Text entry within a video game
JP5087273B2 (ja) 2003-06-18 2012-12-05 ザイ コーポレイション オブ カナダ インコーポレイテッド 設定可能な情報特定システム及び方法
EP1634153A2 (fr) 2003-06-18 2006-03-15 ZI Corporation Systeme et procede d'entree de texte
US7057607B2 (en) 2003-06-30 2006-06-06 Motorola, Inc. Application-independent text entry for touch-sensitive display
US7395203B2 (en) 2003-07-30 2008-07-01 Tegic Communications, Inc. System and method for disambiguating phonetic input
US7389235B2 (en) 2003-09-30 2008-06-17 Motorola, Inc. Method and system for unified speech and graphic user interfaces
US20050075096A1 (en) 2003-10-03 2005-04-07 Aljuraid Nassir Abdulrahman GSM phone applet and method for controlling prayer timings
US7831679B2 (en) 2003-10-15 2010-11-09 Microsoft Corporation Guiding sensing and preferences for context-sensitive services
US6973332B2 (en) 2003-10-24 2005-12-06 Motorola, Inc. Apparatus and method for forming compound words
US8136050B2 (en) 2003-11-21 2012-03-13 Nuance Communications, Inc. Electronic device and user interface and input method therefor
US7224292B2 (en) 2004-04-29 2007-05-29 Research In Motion Limited Reduced keyboard letter selection system and method
US8095364B2 (en) 2004-06-02 2012-01-10 Tegic Communications, Inc. Multimodal disambiguation of speech recognition
US7508324B2 (en) 2004-08-06 2009-03-24 Daniel Suraqui Finger activated reduced keyboard and a method for performing text input
EP1794004A4 (fr) 2004-08-13 2012-05-09 Examples Inc 5 Clavier a une rangee et dactylographie approximative
WO2006026908A1 (fr) 2004-08-25 2006-03-16 Dong Li Procede permettant d'introduire dans un terminal portatif des caracteres chinois a l'aide de lettres phonetiques continues
US8161020B2 (en) 2004-11-15 2012-04-17 Zi Corporation Of Canada, Inc. Searching for and providing objects using byte-by-byte comparison
US20060129928A1 (en) 2004-12-02 2006-06-15 Weigen Qiu Use of pointing device to identify ideographic characters
RU2007125666A (ru) 2004-12-07 2009-01-20 Зи Корпорейшн Оф Канада, Инк. (Ca) Пользовательский интерфейс с расширенными характеристиками поиска
US20060155536A1 (en) 2004-12-20 2006-07-13 Williams Roland E Method and device for displaying a telephone number
US7466859B2 (en) 2004-12-30 2008-12-16 Motorola, Inc. Candidate list enhancement for predictive text input in electronic devices
US9165280B2 (en) * 2005-02-22 2015-10-20 International Business Machines Corporation Predictive user modeling in user interface design
KR101145986B1 (ko) 2005-02-28 2012-05-21 지 데쿠마 아베 세그먼트화-기반 인식
CN101128838B (zh) 2005-02-28 2011-11-16 Zi德库玛股份公司 辨认图
US8036878B2 (en) 2005-05-18 2011-10-11 Never Wall Treuhand GmbH Device incorporating improved text input mechanism
US9842143B2 (en) 2005-11-21 2017-12-12 Zi Corporation Of Canada, Inc. Information delivery system and method for mobile appliances
KR100689821B1 (ko) 2006-03-15 2007-03-08 삼성전자주식회사 휴대용 단말기의 문자 입력 장치 및 방법
US8018439B2 (en) 2006-03-22 2011-09-13 Zi Corporation Of Canada, Inc. Mobile appliance system and method enabling efficient entry
US8166418B2 (en) 2006-05-26 2012-04-24 Zi Corporation Of Canada, Inc. Device and method of conveying meaning
US7925986B2 (en) 2006-10-06 2011-04-12 Veveo, Inc. Methods and systems for a linear character selection display interface for ambiguous text input
WO2008066441A1 (fr) 2006-12-01 2008-06-05 Zi Decuma Ab Procédé permettant une reconnaissance de caractères
US8103499B2 (en) 2007-03-22 2012-01-24 Tegic Communications, Inc. Disambiguation of telephone style key presses to yield Chinese text using segmentation and selective shifting
KR101058139B1 (ko) 2009-07-23 2011-08-25 윤기섭 이동통신단말기의 영문자 입력장치 및 그 방법

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1256875A1 (fr) * 2001-05-10 2002-11-13 Nokia Corporation Procédé et dispositif pour la prédiction des saisies de l'utilisateur selon le contexte
WO2004110040A1 (fr) * 2003-06-10 2004-12-16 Symbian Software Limited Procede permettant a un dispositif d'information sans fil de modifier automatiquement son comportement
EP1522920A2 (fr) * 2003-09-05 2005-04-13 Samsung Electronics Co., Ltd. Interface utilisateur proactive avec un agent émotionnel

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
See also references of WO2007124364A2 *

Also Published As

Publication number Publication date
WO2007124364A3 (fr) 2008-04-10
EP2013761B1 (fr) 2016-11-02
US20140372345A1 (en) 2014-12-18
CN101432722A (zh) 2009-05-13
US20060247915A1 (en) 2006-11-02
CN105528143A (zh) 2016-04-27
US8938688B2 (en) 2015-01-20
US9626355B2 (en) 2017-04-18
WO2007124364B1 (fr) 2008-05-22
EP2013761A4 (fr) 2012-01-04
WO2007124364A2 (fr) 2007-11-01

Similar Documents

Publication Publication Date Title
US9626355B2 (en) Contextual prediction of user words and user actions
CA2537934C (fr) Prevision contextuelle de mots utilisateurs et d&#39;actions utilisateurs
US20100122164A1 (en) Contextual prediction of user words and user actions
US9026428B2 (en) Text/character input system, such as for use with touch screens on mobile phones
US7088861B2 (en) System and method for chinese input using a joystick
CN105759983B (zh) 电子设备的文本输入系统及文本输入方法
US9086736B2 (en) Multiple predictions in a reduced keyboard disambiguating system
KR100766169B1 (ko) 컴퓨터-구현 사전 학습 방법 및 상기 방법을 사용하는 장치, 입력 방법 및 상기 입력 방법을 사용하는 사용자 단말기 장치
EP2133772B1 (fr) Procédé et dispositif incorporant un mécanisme amélioré de saisie de texte
RU2206118C2 (ru) Система устранения неоднозначности с уменьшенной клавиатурой
TWI398795B (zh) 用於資料登錄介面之裝置方法及系統
KR100891358B1 (ko) 사용자의 다음 문자열 입력을 예측하는 글자 입력 시스템및 그 글자 입력 방법
US20130046544A1 (en) Multimodal text input system, such as for use with touch screens on mobile phones
US20110270786A1 (en) Predicting Candidates Using Input Scopes
US20030112277A1 (en) Input of data using a combination of data input systems
WO2008000739A2 (fr) Prédiction cotextuelle
JP2009532773A (ja) キーパッドの複数文字キーを使用した英数字データ入力装置および方法

Legal Events

Date Code Title Description
PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

17P Request for examination filed

Effective date: 20081021

AK Designated contracting states

Kind code of ref document: A2

Designated state(s): AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HU IE IS IT LI LT LU LV MC MT NL PL PT RO SE SI SK TR

AX Request for extension of the european patent

Extension state: AL BA HR MK RS

RIN1 Information on inventor provided before grant (corrected)

Inventor name: LONGE, MICHAEL

Inventor name: VAN MEURS, PIM

Inventor name: BRADFORD, ETHAN, R.

Inventor name: KAY, DAVID JON

Inventor name: TANDON, GAURAV

A4 Supplementary search report drawn up and despatched

Effective date: 20111207

RIC1 Information provided on ipc code assigned before grant

Ipc: G06F 17/27 20060101ALI20111201BHEP

Ipc: G06F 3/048 20060101ALI20111201BHEP

Ipc: G06F 3/00 20060101ALI20111201BHEP

Ipc: G06F 9/44 20060101ALI20111201BHEP

Ipc: G06F 3/023 20060101ALI20111201BHEP

Ipc: H04M 1/725 20060101AFI20111201BHEP

DAX Request for extension of the european patent (deleted)
17Q First examination report despatched

Effective date: 20140409

REG Reference to a national code

Ref country code: DE

Ref legal event code: R079

Ref document number: 602007048567

Country of ref document: DE

Free format text: PREVIOUS MAIN CLASS: G06F0015180000

Ipc: G06F0009455000

GRAP Despatch of communication of intention to grant a patent

Free format text: ORIGINAL CODE: EPIDOSNIGR1

RIC1 Information provided on ipc code assigned before grant

Ipc: G06F 9/455 20060101AFI20160216BHEP

Ipc: G06F 17/27 20060101ALI20160216BHEP

Ipc: G06N 5/04 20060101ALI20160216BHEP

Ipc: G06N 99/00 20100101ALI20160216BHEP

Ipc: G06F 3/0482 20130101ALI20160216BHEP

Ipc: H04M 1/725 20060101ALI20160216BHEP

Ipc: G06F 3/0481 20130101ALI20160216BHEP

INTG Intention to grant announced

Effective date: 20160323

GRAS Grant fee paid

Free format text: ORIGINAL CODE: EPIDOSNIGR3

GRAJ Information related to disapproval of communication of intention to grant by the applicant or resumption of examination proceedings by the epo deleted

Free format text: ORIGINAL CODE: EPIDOSDIGR1

GRAL Information related to payment of fee for publishing/printing deleted

Free format text: ORIGINAL CODE: EPIDOSDIGR3

INTC Intention to grant announced (deleted)
GRAR Information related to intention to grant a patent recorded

Free format text: ORIGINAL CODE: EPIDOSNIGR71

GRAA (expected) grant

Free format text: ORIGINAL CODE: 0009210

AK Designated contracting states

Kind code of ref document: B1

Designated state(s): AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HU IE IS IT LI LT LU LV MC MT NL PL PT RO SE SI SK TR

INTG Intention to grant announced

Effective date: 20160927

REG Reference to a national code

Ref country code: GB

Ref legal event code: FG4D

REG Reference to a national code

Ref country code: AT

Ref legal event code: REF

Ref document number: 842472

Country of ref document: AT

Kind code of ref document: T

Effective date: 20161115

Ref country code: CH

Ref legal event code: EP

REG Reference to a national code

Ref country code: IE

Ref legal event code: FG4D

REG Reference to a national code

Ref country code: DE

Ref legal event code: R096

Ref document number: 602007048567

Country of ref document: DE

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: LV

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20161102

REG Reference to a national code

Ref country code: NL

Ref legal event code: MP

Effective date: 20161102

REG Reference to a national code

Ref country code: LT

Ref legal event code: MG4D

REG Reference to a national code

Ref country code: AT

Ref legal event code: MK05

Ref document number: 842472

Country of ref document: AT

Kind code of ref document: T

Effective date: 20161102

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: NL

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20161102

Ref country code: GR

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20170203

Ref country code: LT

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20161102

Ref country code: SE

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20161102

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: FI

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20161102

Ref country code: PL

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20161102

Ref country code: PT

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20170302

Ref country code: ES

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20161102

Ref country code: AT

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20161102

Ref country code: IS

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20170302

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: RO

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20161102

Ref country code: DK

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20161102

Ref country code: EE

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20161102

Ref country code: CZ

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20161102

Ref country code: SK

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20161102

REG Reference to a national code

Ref country code: DE

Ref legal event code: R097

Ref document number: 602007048567

Country of ref document: DE

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: IT

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20161102

Ref country code: BE

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20161102

Ref country code: BG

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20170202

PLBE No opposition filed within time limit

Free format text: ORIGINAL CODE: 0009261

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: NO OPPOSITION FILED WITHIN TIME LIMIT

26N No opposition filed

Effective date: 20170803

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: SI

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20161102

REG Reference to a national code

Ref country code: CH

Ref legal event code: PL

GBPC Gb: european patent ceased through non-payment of renewal fee

Effective date: 20170419

REG Reference to a national code

Ref country code: IE

Ref legal event code: MM4A

REG Reference to a national code

Ref country code: FR

Ref legal event code: ST

Effective date: 20171229

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: FR

Free format text: LAPSE BECAUSE OF NON-PAYMENT OF DUE FEES

Effective date: 20170502

Ref country code: MC

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20161102

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: CH

Free format text: LAPSE BECAUSE OF NON-PAYMENT OF DUE FEES

Effective date: 20170430

Ref country code: LU

Free format text: LAPSE BECAUSE OF NON-PAYMENT OF DUE FEES

Effective date: 20170419

Ref country code: GB

Free format text: LAPSE BECAUSE OF NON-PAYMENT OF DUE FEES

Effective date: 20170419

Ref country code: LI

Free format text: LAPSE BECAUSE OF NON-PAYMENT OF DUE FEES

Effective date: 20170430

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: IE

Free format text: LAPSE BECAUSE OF NON-PAYMENT OF DUE FEES

Effective date: 20170419

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: MT

Free format text: LAPSE BECAUSE OF NON-PAYMENT OF DUE FEES

Effective date: 20170419

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: HU

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT; INVALID AB INITIO

Effective date: 20070419

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: CY

Free format text: LAPSE BECAUSE OF NON-PAYMENT OF DUE FEES

Effective date: 20161102

PG25 Lapsed in a contracting state [announced via postgrant information from national office to epo]

Ref country code: TR

Free format text: LAPSE BECAUSE OF FAILURE TO SUBMIT A TRANSLATION OF THE DESCRIPTION OR TO PAY THE FEE WITHIN THE PRESCRIBED TIME-LIMIT

Effective date: 20161102

PGFP Annual fee paid to national office [announced via postgrant information from national office to epo]

Ref country code: DE

Payment date: 20230222

Year of fee payment: 17